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| United States Patent |
5,510,981
|
|
Berger
, et al.
|
April 23, 1996
|
Language translation apparatus and method using context-based
translation models
Abstract
An apparatus for translating a series of source words in a first language
to a series of target words in a second language. For an input series of
source words, at least two target hypotheses, each including a series of
target words, are generated. Each target word has a context comprising at
least one other word in the target hypothesis. For each target hypothesis,
a language model match score including an estimate of the probability of
occurrence of the series of words in the target hypothesis. At least one
alignment connecting each source word with at least one target word in the
target hypothesis is identified. For each source word and each target
hypothesis, a word match score including an estimate of the conditional
probability of occurrence of the source word, given the target word in the
target hypothesis which is connected to the source word and given the
context in the target hypothesis of the target word which is connected to
the source word. For each target hypothesis, a translation match score
including a combination of the word match scores for the target hypothesis
and the source words in the input series of source words. A target
hypothesis match score including a combination of the language model match
score for the target hypothesis and the translation match score for the
target hypothesis. The target hypothesis having the best target hypothesis
match score is output.
| Inventors:
|
Berger; Adam L. (New York, NY);
Brown; Peter F. (New York, NY);
Della Pietra; Stephen A. (Pearl River, NY);
Della Pietra; Vincent J. (Blauvelt, NY);
Kehler; Andrew S. (Somerville, MA);
Mercer; Robert L. (Yorktown Heights, NY)
|
| Assignee:
|
International Business Machines Corporation (Armonk, NY)
|
| Appl. No.:
|
144913 |
| Filed:
|
October 28, 1993 |
| Current U.S. Class: |
704/2; 704/9; 704/231; 704/257; 704/277; 715/536 |
| Intern'l Class: |
G06F 017/28 |
| Field of Search: |
364/419.02,419.08,419.16,200 MS File
381/43,51
|
References Cited
U.S. Patent Documents
| 4754489 | Jun., 1988 | Bokder | 382/40.
|
| 4829580 | May., 1989 | Church | 381/52.
|
| 4882759 | Nov., 1989 | Bahl et al. | 381/51.
|
| 5033087 | Jul., 1991 | Bahl et al. | 381/43.
|
| 5068789 | Nov., 1991 | Van Vliembergen | 364/419.
|
| 5109509 | Apr., 1992 | Katayama et al. | 395/600.
|
| 5146405 | Sep., 1992 | Church | 364/419.
|
| 5200893 | Apr., 1993 | Dzawa et al. | 364/419.
|
Other References
Brown, P. F., et al. "Analysis, Statistical Transfer, and Synthesis in
Machine Translation." Proceedings of the Fourth International Conference
on Theoretical and Methodological Issues in Machine Translation, Nov.
1992, pp. 83-100.
Brown, Peter F., et al. "Class-Based N-Gram Models of Natural Language."
Computational Linguistics, vol. 18, No. 4, Dec. 1992, pp. 467-480.
Brown, Peter F., et al. "The Mathematics of Statistical Machine
Translation: Parameter Estimation." Computational Linguistics, vol. 19,
No. 2, Jun. 1993, pp. 263-311.
Brown, Peter F., et al. "Method and Apparatus For Natural Language
Translation." U.S. patent application Ser. No. 07/736,278, filed Jul. 25,
1991.
Brown, P. F. et al. "Word Sense Disambiguation Using Statistical Methods."
Proceedings 29th Annual Meeting of the Association for Computational
Linguistics, Berkeley, California, Jun. 1991, pp. 265-270.
Darroch, J. N. et al. "Generalized Iterative Scaling for Log-Linear
Models." The Annals of Mathematical Statistics, vol. 43, No. 5, 1972, pp.
1470-1480.
|
Primary Examiner: Huntley; David M.
Assistant Examiner: Poinvil; Frantzy
Attorney, Agent or Firm: Schechter; Marc D., Tassinari; Robert P.
Goverment Interests
This invention was made with Government support under Contract No.
N00014-91-C-0135 awarded by the Office of Naval Research. The Government
has certain rights in this invention.
Claims
We claim:
1. An apparatus for translating a series of source words in a first
language to a series of target words in a second language different from
the first language, said apparatus comprising:
means for inputting said series of source words;
means for generating at least two target hypotheses, each target hypothesis
comprising said series of target words selected from a vocabulary of words
in the second language, each target word having a context comprising at
least one other word in the target hypothesis;
means for generating, for each target hypothesis, a language model match
score comprising an estimate of the probability of occurrence of the
series of words in the target hypothesis;
means for identifying at least one alignment between the input series of
source words and each target hypothesis, the alignment connecting each
source word with at least one target word in the target hypothesis;
means for generating, for each source word and each target hypothesis, a
word match score comprising an estimate of the conditional probability of
occurrence of the source word, given the target word in the target
hypothesis which is connected to the source word and given the context of
the target word in the target hypothesis which is connected to the source
word;
means for generating, for each target hypothesis, a translation match score
comprising a combination of the word match scores for the target
hypothesis and the source words in the input series of source words;
means for generating a target hypothesis match score for each target
hypothesis, each target hypothesis match score comprising a combination of
the language model match score for the target hypothesis and the
translation match score for the target hypothesis; and
means for outputting the target hypothesis having the best target
hypothesis match score.
2. An apparatus as claimed in claim 1, characterized in that each target
hypothesis comprises a series of target words selected from a vocabulary
comprising words in the second language and a null word representing the
absence of a word.
3. An apparatus as claimed in claim 2, characterized in that:
the means for identifying at least one alignment comprises means for
identifying two or more alignments between the input series of source
words and each target hypothesis, each alignment connecting each source
word with at least one target word in the target hypothesis;
the word match score generator comprises means for generating, for each
source word and each alignment and each target hypothesis, a word match
score comprising an estimate of the conditional probability of occurrence
of the source word, given the target word in the target hypothesis which
is connected to the source word and given the context of the target word
in the target hypothesis which is connected to the source word;
the translation match score generator comprises means for generating, for
each target hypothesis, a translation match score comprising a combination
of the word match scores for the target hypothesis and the alignments and
the source words in the input series of source words.
4. An apparatus as claimed in claim 2, characterized in that:
the input means comprises means for transforming the input series of source
words into a series of transformed source words;
the alignment means comprises means for identifying at least one alignment
between the series of transformed source words and each target hypothesis,
the alignment connecting each transformed source word with at least one
target word in the target hypothesis;
the word match score generator comprises means for generating, for each
transformed source word and each target hypothesis, a word match score
comprising an estimate of the conditional probability of occurrence of the
transformed source word, given the target word in the target hypothesis
which is connected to the transformed source word and given the context of
the target word in the target hypothesis which is connected to the
transformed source word;
the translation match score generator comprises means for generating for
each target hypothesis, a translation match score comprising a combination
of the word match scores for the target hypothesis and the transformed
source words; and
the output means comprises means for synthesizing a series of output words
from the target hypothesis having the best target hypothesis match score,
and means for outputting the synthesized output words.
5. An apparatus as claimed in claim 2, characterized in that:
the translation match score for a target hypothesis comprises the product
of the word match scores for the target hypothesis and the source words in
the input series of source words; and
the target hypothesis match score for a target hypothesis comprises the
product of the language model match score for the target hypothesis
multiplied by the translation match score for the target hypothesis.
6. An apparatus as claimed in claim 2, characterized in that:
the context of the target word in the target hypothesis which is connected
to a source word is contained in at least one of two or more context
classes; and
the estimated conditional probability of occurrence of the source word,
given the target word in the target hypothesis which is connected to the
source word and given the context of the target word which is connected to
the source word, comprises at least one function having a value dependent
on the class containing the context of the target word which is connected
to the source word.
7. An apparatus as claimed in claim 2, characterized in that:
the context of the target word in the target hypothesis which is connected
to a source word comprises at least one word having a part of speech in
the target hypothesis; and
the estimated conditional probability of occurrence of the source word,
given the target word in the target hypothesis which is connected to the
source word and given the context of the target word which is connected to
the source word, comprises at least one function having a value dependent
on the part of speech in the target hypothesis of at least one word in the
context of the target word which is connected to the source word.
8. An apparatus as claimed in claim 2, characterized in that:
the context of the target word in the target hypothesis which is connected
to a source word comprises at least one word having an identity; and
the estimated conditional probability of occurrence of the source word,
given the target word in the target hypothesis which is connected to the
source word and given the context of the target word which is connected to
the source word, comprises at least one function having a value dependent
on the identity of at least one word in the context of the target word
which is connected to the source word.
9. An apparatus as claimed in claim 2, characterized in that the means for
outputting the target hypothesis having the best match score comprises a
display.
10. An apparatus as claimed in claim 2, characterized in that the input
means comprises a keyboard.
11. An apparatus as claimed in claim 2, characterized in that the input
means comprises a computer disk drive.
12. An apparatus as claimed in claim 2, characterized in that the input
means comprises a computer tape drive.
13. A computer-implemented method of translating a series of source words
in a first language to a series of target words in a second language
different from the first language, said method comprising:
inputting a series of source words;
generating at least two target hypotheses, each target hypothesis
comprising said series of target words selected from a vocabulary of words
in the second language, each target word having a context comprising at
least one other word in the target hypothesis;
generating, for each target hypothesis, a language model match score
comprising an estimate of the probability of occurrence of the series of
words in the target hypothesis;
identifying at least one alignment between the input series of source words
and each target hypothesis, the alignment connecting each source word with
at least one target word in the target hypothesis;
generating, for each source word and each target hypothesis, a word match
score comprising an estimate of the conditional probability of occurrence
of the source word, given the target word in the target hypothesis which
is connected to the source word and given the context of the target word
in the target hypothesis which is connected to the source word;
generating, for each target hypothesis, a translation match score
comprising a combination of the word match scores for the target
hypothesis and the source words in the input series of source words;
generating a target hypothesis match score for each target hypothesis, each
target hypothesis match score comprising a combination of the language
model match score for the target hypothesis and the translation match
score for the target hypothesis; and
outputting the target hypothesis having the best target hypothesis match
score.
14. A method as claimed in claim 13, characterized in that each target
hypothesis comprises a series of target words selected from a vocabulary
comprising words in the second language and a null word representing the
absence of a word.
15. A method as claimed in claim 14, characterized in that:
the step of identifying at least one alignment comprises identifying two or
more alignments between the input series of source words and each target
hypothesis, each alignment connecting each source word with at least one
target word in the target hypothesis;
the step of generating a word match score comprises generating, for each
source word and each alignment and each target hypothesis, a word match
score comprising an estimate of the conditional probability of occurrence
of the source word, given the target word in the target hypothesis which
is connected to the source word and given the context of the target word
in the target hypothesis which is connected to the source word;
the step of generating a translation match score comprises generating, for
each target hypothesis, a translation match score comprising a combination
of the word match scores for the target hypothesis and the alignments and
the source words in the input series of source words.
16. A method as claimed in claim 14, characterized in that:
the step of inputting comprises the step of transforming the input series
of source words into a series of transformed source words;
the step of aligning comprises identifying at least one alignment between
the series of transformed source words and each target hypothesis, the
alignment connecting each transformed source word with at least one target
word in the target hypothesis;
the step of generating a word match score comprises generating, for each
transformed source word and each target hypothesis, a word match score
comprising an estimate of the conditional probability of occurrence of the
transformed source word, given the target word in the target hypothesis
which is connected to the transformed source word and given the context of
the target word in the target hypothesis which is connected to the
transformed source word;
the step of generating a translation match score comprises generating, for
each target hypothesis, a translation match score comprising a combination
of the word match scores for the target hypothesis and the transformed
source words; and
the step of outputting comprises the step of synthesizing a series of
output words from the target hypothesis having the best target hypothesis
match score.
17. A method as claimed in claim 14, characterized in that:
the translation match score for a target hypothesis comprises the product
of the word match scores for the target hypothesis and the source words in
the input series of source words; and
the target hypothesis match score for a target hypothesis comprises the
product of the language model match score for the target hypothesis
multiplied by the translation match score for the target hypothesis.
18. A method as claimed in claim 14, characterized in that:
the context of the target word in the target hypothesis which is connected
to a source word is contained in at least one of two or more context
classes; and
the estimated conditional probability of occurrence of the source word,
given the target word in the target hypothesis which is connected to the
source word and given the context of the target word which is connected to
the source word, comprises at least one function having a value dependent
on the class containing the context of the target word which is connected
to the source word.
19. A method as claimed in claim 14, characterized in that:
the context of the target word in the target hypothesis which is connected
to a source word comprises at least one word having a part of speech in
the target hypothesis; and
the estimated conditional probability of occurrence of the source word,
given the target word in the target hypothesis which is connected to the
source word and given the context of the target word which is connected to
the source word, comprises at least one function having a value dependent
on the part of speech in the target hypothesis of at least one word in the
context of the target word which is connected to the source word.
20. A method as claimed in claim 14, characterized in that:
the context of the target word in the target hypothesis which is connected
to a source word comprises at least one word having an identity; and
the estimated conditional probability of occurrence of the source word,
given the target word in the target hypothesis which is connected to the
source word and given the context of the target word which is connected to
the source word, comprises at least one function having a value dependent
on the identity of at least one word in the context of the target word
which is connected to the source word.
21. A method as claimed in claim 14, characterized in that the step of
outputting the target hypothesis having the best match score comprises
displaying the target hypothesis having the best match score.
Description
BACKGROUND OF THE INVENTION
The invention relates to computerized language translation, such as
computerized translation of a French sentence into an English sentence.
In U.S. patent application Ser. No. 07/736,278, filed Jul. 25, 1991, now
pending, entitled "Method and System for Natural Language Translation" by
Peter F. Brown et al (the entire content of which is incorporate herein by
reference), there is described a computerized language translation system
for translating a text F in a source language to a text E in a target
language. The system described therein evaluates, for each of a number of
hypothesized target language texts E, the conditional probability
P(E.vertline.F) of the target language test E given the source language
text F. The hypothesized target language text E having the highest
conditional probability P(E.vertline.F) is selected as the translation of
the source language text F.
Using Bayes' theorem, the conditional probability P(E.vertline.F) of the
target language text E given the source language text F can be written as
##EQU1##
Since the probability P(F) of the source language text F in the denominator
of Equation 1 is independent of the target language text E, the target
language text E having the highest conditional probability P(E.vertline.F)
will also have the highest product P(F.vertline.E) P(E). We therefore
arrive at
##EQU2##
In Equation 2, the probability P(E) of the target language text E is a
language model match score and may be estimated from a target language
model. While any known language model may be used to estimate the
probability P(E) of the target language text E, Brown et al describe an
n-gram language model comprising a 1-gram model, a 2-gram model, and a
3-gram model combined by parameters whose values are obtained by
interpolated estimation.
The conditional probability P(F.vertline.E) in Equation 2 is a translation
match score. As described by Brown et al, the translation match score
P(F.vertline.E) for a source text F comprising a series of source words,
given a target hypothesis E comprising a series of target words, may be
estimated by finding all possible alignments connecting the source words
in the source text F with the target words in the target text E, including
alignments in which one or more source words are not connected to any
target words, but not including alignments where a source word is
connected to more than one target word. For each alignment and each target
word e in the target text E connected to .phi. source words in the source
text F, there is estimated the fertility probability n(.phi..vertline.e)
that the target word e is connected to the .phi. source words in the
alignment. There is also estimated for each source word f in the source
text F and each target word e in the target text E connected to the source
word f by the alignment, the lexical probability t(f.vertline.e) that the
source word f would occur given the occurrence of the connected target
word e.
For each alignment and each source word f, Brown et al further estimate the
distortion probability a(j.vertline.a.sub.j,m) that the source word f is
located in position j of the source text F, given that the target word e
connected to the source word f is located in position a.sub.j in the
target text E, and given that there are m words in the source text F.
By combining the fertility probabilities for an alignment and for all
target words e in the target text E, and multiplying the result by the
probability
##EQU3##
of the number .phi..sub.0 of target words not connected with any source
words in the alignment, given the sum of the fertilities .phi. of all of
the target words in the target text E in the alignment, a fertility score
for the target text E and the alignment is obtained.
By combining the lexical probabilities for an alignment and for all source
words in the source text F, a lexical score for the alignment is obtained.
By combining the distortion probabilities for an alignment and for all
source words in the source text F which are connected to a target word in
the alignment, and by multiplying the result by
##EQU4##
(where .phi..sub.0 is the number of target words in the target text E that
are not connected with any source words), a distortion score for the
alignment is obtained.
Finally, by combining the fertility, lexical, and distortion scores for the
alignment, and multiplying the result by the combinatorial factor
##EQU5##
a translation match score for the alignment is obtained. (See, Brown et
al, Section 8.2.)
The translation match score P(F.vertline.E) for the source text F and the
target hypothesis E may be the sum of the translation match scores for all
permitted alignments between the source text F and the target hypothesis
E. Preferably, the translation match score P(F.vertline.E) for the source
text F and the target hypothesis E is the translation match score for the
alignment estimated to be most probable.
Equation 2 may be used to directly estimate the target hypothesis match
score P(F.vertline.E)P(E) for a hypothesized target language text E and a
source language text F. However, in order to simply the language model
P(E) and the translation model P(F.vertline.E), and in order to estimate
the parameters of these models from a manageable amount of training data,
Brown et al estimate the target hypothesis match score P(F.vertline.E)P(E)
for simplified intermediate forms E' and F' of the target language text E
and the source language text F, respectively. Each intermediate target
language word e' represents a class of related target language words. Each
intermediate source language word f' represents a class of related source
language words. A source language transducer converts the source language
text F to the intermediate form F'. The hypothesized intermediate form
target language text E' having the highest hypothesis match score
P(F'.vertline.E')P(E') is estimated from Equation 2. A target language
transducer converts the best matched intermediate target language text E'
to the target language text E.
In their language translation system, Brown et al estimate the lexical
probability of each source word f as the conditional probability
t(f.vertline.e) of each source word f given solely the target word e
connected to the source word in an alignment. Consequently, the lexical
probability provides only a coarse estimate of the probability of the
source word f.
SUMMARY OF THE INVENTION
It is an object of the invention to provide an apparatus and method for
translating a series of source words in a first language to a series of
target words in a second language different from the first language, in
which the apparatus and method provide improved estimates of the lexical
probabilities of the source words.
It is another object of the invention to provide an apparatus and method
for translating a series of source words in a first language to a series
of target words in a second language different from the first language, in
which the lexical probability of a source word is estimated as a
conditional probability given the target word connected to the source word
in an alignment, and given the context of the target word connected to the
source word.
According to the invention, an apparatus for translating a series of source
words in a first language to a series of target words in a second language
different from the first language comprises means for inputting a series
of source words. Means are also provided for generating at least two
target hypotheses. Each target hypothesis comprises a series of target
words selected from a vocabulary of words in the second language. Each
target word has a context comprising at least one other word in the target
hypothesis.
A language model match score generator generates, for each target
hypothesis, a language model match score comprising an estimate of the
probability of occurrence of the series of words in the target hypothesis.
An alignment identifier identifies at least one alignment between the
input series of source words and each target hypothesis. The alignment
connects each source word with at least one target word in the target
hypothesis.
A word match score generator is provided for generating, for each source
word and each target hypothesis, a word match score comprising an estimate
of the conditional probability of occurrence of the source word, given the
target word in the target hypothesis which is connected to the source word
and given the context of the target word in the target hypothesis which is
connected to the source word. A translation match score generator
generates, for each target hypothesis, a translation match score
comprising a combination of the word match scores for the target
hypothesis and the source words in the input series of source words.
A hypothesis match score generator is provided for generating a target
hypothesis match score for each target hypothesis. Each target hypothesis
match score comprises a combination of the language model match score for
the target hypothesis and the translation match score for the target
hypothesis. The target hypothesis having the best target hypothesis match
score is provided at an output.
Preferably, each target hypothesis comprises a series of target words
selected from a vocabulary comprising words in the second language and a
null word representing the absence of a word.
The alignment identifier may comprise means for identifying two or more
alignments between the input series of source words and each target
hypothesis. Each alignment connects each source word with at least one
target word in the target hypothesis. The word match score generator
generates, for each source word and each alignment and each target
hypothesis, a word match score comprising an estimate of the conditional
probability of occurrence of the source word, given the target word
connected to the source word, and given the context of the target word.
The translation match score generator generates, for each target
hypothesis, a translation match score comprising a combination of the word
match scores for the target hypothesis and the alignments and the source
words in the input series of source words.
The source text input device may comprise means for transforming the input
series of source words into a series of transformed source words. The
alignment means identifies at least one alignment between the series of
transformed source words and each target hypothesis. Each alignment
connects each transformed source word with at least one target word in the
target hypothesis. The word match score generator generates, for each
transformed source word and each target hypothesis, a word match score
comprising an estimate of the conditional probability of occurrence of the
transformed source word, given the target word in the target hypothesis
which is connected to the transformed word and given the context of the
target word in the target hypothesis which is connected to the transformed
source word.
The translation match score generator generates, for each target
hypothesis, a translation match score comprising a combination of the word
match scores for the target hypothesis and the transformed source word.
Output means are provided for synthesizing a series of output words from
the target hypothesis having the best target hypothesis match score, and
for outputting the output words.
The translation match score for a target hypothesis may comprise the
product of the word match scores for the target hypothesis and the source
words in the input series of source words. The target hypothesis match
score for a target hypothesis may comprise the product of the language
model match score for the target hypothesis multiplied by the translation
match score for the target hypothesis.
The context of the target word in the target hypothesis which is connected
to a source word may be contained in at least one of two or more context
classes. The estimated conditional probability of occurrence of the source
word, given the target word connected to the source word and given the
context of the target word, comprises at least one function having a value
dependent on the class containing the context of the target word.
Alternatively, the estimated conditional probability of occurrence of the
source word may comprise a function having a value dependent on the part
of speech in the target hypothesis of at least one word in the context of
the target word which is connected to the source word, or dependent on the
identity of at least one word in the context of the target word which is
connected to the source word.
The means for outputting the target hypothesis having the best match score
may comprise a display. The input means may comprise a keyboard, a
computer disk drive, or a computer tape drive.
By estimating the lexical probability of a source word as the conditional
probability of the source word given the target word connected to the
source word in an alignment, and given the context of the target word
connected to the source word, the present invention obtains translation
match scores with improved accuracy.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of an example of an apparatus for translating
words from a first language to a second language according to the present
invention.
FIG. 2 schematically shows an example of an alignment between a
hypothetical series of source words and a hypothetical series of target
words.
FIG. 3 schematically shows an example of a second alignment between the
hypothetical series of source words and the hypothetical series of target
words of FIG. 2.
FIG. 4 schematically shows an example of a third alignment between the
hypothetical series of source words and the hypothetical series of target
words of FIG. 2.
FIG. 5 is a schematic flow diagram of a source transducer.
FIG. 6 is a schematic flow diagram of a token-to-word transducer.
FIG. 7 is a schematic flow diagram of a syntactic transducer for English.
FIG. 8 is a schematic flow diagram of a syntactic transducer for French.
FIG. 9 is an example of a syntactic transduction.
FIG. 10 is a schematic flow diagram illustrating the operation of a
finite-state transducer.
FIG. 11 is Table of Patterns for a finite-state pattern matcher employed in
some source and target transducers.
FIG. 12 is a Table of Actions for an action processing module employed in
some source and target transducers.
FIG. 13 is a schematic block diagram illustrating a manner in which
statistical transfer can be incorporated into a translation system based
on an interlingua.
FIG. 14 is an example of informants and informant sites.
FIG. 15 is a schematic flow diagram illustrating the operation of a
sense-labelling transducer.
FIG. 16 is a schematic flow diagram of a module that determines good
questions about informants for each vocabulary word.
FIG. 17 is a schematic flow diagram of a module that determines a good
question about each informant of a vocabulary word.
FIG. 18 is a schematic flow diagram of a method for determining a good
question about an informant.
FIG. 19 is a schematic block diagram of a simplified French to English
translation system which incorporates analysis and synthesis.
FIG. 20 is a schematic flow diagram of a translation component of a batch
system.
FIG. 21 is a schematic flow diagram of the hypothesis search component of
the system.
FIG. 22 is a schematic flow diagram of a translation component of a
human-aided system.
FIG. 23 is an example of a source sentence being transduced to a sequence
of morphemes.
FIG. 24 is an example of a partial hypothesis which results from an
extension by the target morpheme the.
FIG. 25 is an example of a partial hypothesis which results from an
extension with an open target morpheme.
FIG. 26 is an example of a partial hypothesis which results from an
extension in which an open morpheme is closed.
FIG. 27 is an example of a partial hypothesis which results from an
extension in which an open morpheme is kept open.
FIG. 28 is an example of a partial hypothesis which results from an
extension by the target morpheme to.sub.-- wake.
FIG. 29 is an example of a partial hypothesis which results from an
extension by the pair of target morphemes up and to.sub.-- wake.
FIG. 30 is an example of a partial hypothesis which results from an
extension by the pair of target morphemes up and to.sub.-- wake in which
to.sub.-- wake is open.
FIG. 31 is an example of a subset lattice.
FIG. 32 is an example of a partial hypothesis that is stored in the
priority queue {2,3}.
FIG. 33 is a schematic flow diagram of the process by which partial
hypothesis are selected for extension.
FIG. 34 is a schematic flow diagram of the method by which the partial
hypotheses on a priority queue are processed in the selection for
extension step.
FIG. 35 contains pseudocode describing the method for extending hypotheses.
FIG. 36 is a schematic block diagram of a target structure language model.
FIG. 37 is a schematic block digram of a method for partitioning a
vocabulary into classes.
FIG. 38 is a schematic flow diagram of a method for partitioning a
vocabulary into classes.
FIG. 39 shows some sample subtrees from a tree constructed using a
clustering method.
FIG. 40 is a schematic flow diagram of a method for aligning sentences in
parallel corpora.
FIG. 41 is a schematic flow diagram of the basic step of a method for
aligning sentences.
FIG. 42 depicts a sample of text before and after textural cleanup and
sentence detection.
FIG. 43 shows an example of a division of aligned corpora into beads.
FIG. 44 shows a finite state model for generating beads.
FIG. 45 shows the beads that are allowed by the model.
FIG. 46 is a histogram of French sentence lengths.
FIG. 47 is a histogram of English sentence lengths.
FIG. 48 is a schematic diagram of a process by which word-by-word
correspondences are extracted from a bilingual corpus.
FIG. 49 is an example of a partial hypothesis which results from an
extension by the target morpheme mother.
FIG. 50 is a schematic flow diagram of a basic system operating in batch
mode.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
FIG. 1 is a block diagram of an example of an apparatus for translating
words from a first language to a second language according to the present
invention. The apparatus comprises a source text input device 10 for
inputting a series of source words. The source text input device 10 may
comprise, for example, a keyboard, a computer disk drive, or a computer
tape drive.
The source text input device 10 may further comprise means for transforming
the input series of source words into a series of transformed source
words. Each transformed source word may represent a set of related input
source words. For example, each of the input source words in the set
{etre, etant, ete, suis, es, est, sommes, etes, sont, fus, fumes, serai,
serons, sois, soit, soyons, soyez, soient} are forms of the verb
infinitive "etre", and may be transformed to "etre" with a tag
representing the tense of the original input source word.
The means for transforming the input series of source words into a series
of transformed source words may comprise the source transducers described
in Sections 3, 4 and 11 of Brown et al, above, which are incorporated
herein by reference and set forth in full herein. In essence, these
transducers check and correct the spelling of the input source words,
check and correct the case of the input source words, detect titles of
documents in the input series of source words, and detect names in the
input series of source words. The transducers also tag each input source
word with the most likely part of speech of the source word, and flag
unknown source words (which are not contained in a stored vocabulary of
source words). The means for transforming the input series of source words
also collapses multi-word units of input source words into single
transformed source words, and splits compound input source words into two
or more transformed source words. The means for transforming the input
series of source words into a series of transformed source words further
performs linguistic or morphological transformations of different forms of
a word into a single basic form. Finally, the means for transforming the
input series of source words may also estimate the sense of each input
source word, and assign that sense to the transformed source word.
Transducers described in Brown et al are now described in detail.
SOURCE TRANSDUCERS (Brown et al. Section 3)
In this section, some embodiments of the the source-transducer 701 will be
explained. The role of this transducer is to produce one or more
intermediate source-structure representations from a portion of text in
the source language.
Overview (Brown et al. Section 3.1)
An embodiment of the source-transducer 701 is shown in FIG. 5. In this
embodiment, the transducer takes as input a sentence in the source
language and produces a single intermediate source-structure consisting of
a sequence of linguistic morphs. This embodiment of the source-transducer
701 comprises transducers that:
tokenize raw text 1101;
determine words from tokens 1102;
annotate words with parts of speech 1103;
perform syntactic transformations 1104;
perform morphological transformations 1105;
annotate morphs with sense labels 1106.
It should be understood that FIG. 5 represents only one embodiment of the
source-transducer 701. Many variations are possible. For example, the
transducers 1101, 1102, 1103, 1104, 1105, 1106 may be augmented and/or
replaced by other transducers. Other embodiments of the source-transducer
701 may include a transducer that groups words into compound words or
identifies idioms. In other embodiments, rather than a single-intermediate
source-structure being produced for each source sentence, a set of several
intermediate source-structures together with probabilities or scores may
be produced. In such embodiments the transducers depicted in FIG. 5 can be
replaced by transducers which produce at each stage intermediate
structures with probabilities or scores. In addition, the intermediate
source-structures produced may be different. For example, the intermediate
structures may be entire parse trees, or case frames for the sentence,
rather than a sequence of morphological units. In these cases, there may
be more than one intermediate source-structure for each sentence with
scores, or there may be only a single intermediate source-structure.
Components (Brown et al. Section 3.2)
Referring still to FIG. 5, the transducers comprising the source-transducer
701 will be explained. For concreteness, these transducers will be
discussed in cases in which the source language is either English or
French.
Tokenizing Transducers (Brown et al. Section 3.2.1)
The purpose of the first transducer 1101, which tokenizes raw text, is well
illustrated by the following Socratic dialogue.
How do you find words in text?
Words occur between spaces.
What about "however,"? Is that one word or two?
Oh well, you have to separate out the commas.
Periods too?
Of course.
What about "Mr."?
Certain abbreviations have to be handled separately.
How about "shouldn't"? One word or two?
One.
So "shouldn't" is different from "should not"?
Yes.
And "Gauss-Bonnet", as in the "Gauss-Bonnet Theorem"?
Two names, two words.
So if you split words at hyphens, what do you do with "vis-a-vis"? One
word, and don't ask me why.
How about "stingray"?
One word, of course.
And "manta ray"?
One word: it's just like stingray.
But there's a space.
Too bad.
How about "inasmuch as"?
Two.
Are you sure?
No.
This dialogue illustrates that there is no canonical way of breaking a
sequence of characters into tokens. The purpose of the transducer 1101,
that tokenizes raw text, is to make some choice.
In an embodiment in which the source-language is English, this tokenizing
transducer 1101 uses a table of a few thousand special character sequences
which are parsed as single tokens and otherwise treat spaces and hyphens
as word separators. Punctuation marks and digits are separated off and
treated as individual words. For example, 87, is tokenized as the three
words 8 7 and ,.
In another embodiment in which the source-language is French, the
tokenizing transducer 1101 operates in a similar way. In addition to space
and hyphen, the symbol -t- is treated as a separator when tokenizing
French text.
Token-Word Transducers (Brown et al. Section 3.2.2)
The next transducer 1102 determines a sequence of words from a sequence of
token spellings. In some embodiments, this transducer comprises two
transducers as depicted in FIG. 6. The first of these transducers 1201
determines an underlying case pattern for each token in the sequence. The
second of these transducers 1202 performs a number of specialized
transformations. These embodiments can easily be modified or enhanced by a
person skilled in the art.
True-Case Transducers (Brown et al. Section 3.2.3)
The purpose of a true-case transducer 1201 is made apparent by another
Socratic dialogue:
When do two sequences of characters represent the same word?
When they are the same sequences.
So, "the" and "The" are different words?
Don't be ridiculous. You have to ignore differences in case.
So "Bill" and "bill" are the same word?
No, "Bill" is a name and "bill" is something you pay. With proper names the
case matters.
What about the two "May"'s in "May I pay in May?"
The first one is not a proper name. It is capitalized because it is the
first word in the sentence.
Then, how do you know when to ignore case and when not to?
If you are human, you just know.
Computers don't know when case matters and when it doesn't. Instead, this
determination can be performed by a true-case transducer 1201. The input
to this transducer is a sequence of tokens labeled by a case pattern that
specifies the case of each letter of the token as it appears in printed
text. These case patterns are corrupted versions of true-case patterns
that specify what the casing would be in the absence of typographical
errors and arbitrary conventions (e.g., capitalization at the beginning of
sentences). The task of the true-case transducer is to uncover the
true-case patterns.
In some embodiments of the true-case transducer the case and true-case
patterns are restricted to eight possibilities.
##EQU6##
Here U denotes an upper case letter, L a lower case letter, U.sup.+ a
sequence of one or more upper case letters, and L.sup.+ a sequence of one
or more lower case letters. In these embodiments, the true-case
transducers can determine a true-case of an occurrence of a token in text
using a simple method comprising the following steps:
1. Decide whether the token is part of a name. If so, set the true-case
equal to the most probable true-case beginning with a U for that token.
2. If the token is not part of a name, then check if the token is a member
of a set of tokens which have only one true-case. If so, set the true-case
appropriately.
3. If the true-case has not been determined by steps 1 or 2, and the token
is the first token in a sentence then set the true-case equal to the most
probable true-case for that token.
4. Otherwise, set the true-case equal to the case for that token.
In an embodiment of the true-case transducer used for both English and
French, names are recognized with a simple finite-state machine. This
machine employs a list of 12,937 distinct common last names and 3,717
distinct common first names constructed from a list of 125 million full
names obtained from the IBM online phone directory and a list of names
purchased from a marketing corporation. It also uses lists of common
precursors to names (such as Mr., Mrs., Dr., Mlle., etc.) and common
followers to names (such as Jr., Sr., III, etc).
The set of tokens with only one true-case consists of all tokens with a
case-pattern entropy of less than 1.25 bits, together with 9,506 Number of
records in (o+p)8260.lcwtab English and 3,794 records in
r8260.coerce(a+b+c) French tokens selected by hand from the tokens of a
large bilingual corpus. The most probable case pattern for each English
token was determined by counting token-case cooccurences in a 67 million
word English corpus, and for each French token by counting cooccurences in
a 72 million word French corpus. (In this counting occurrences at the
beginnings of sentences are ignored).
Specialized Transformation Transducers (Brown et al. Section 3.2.4)
Referring still to FIG. 6, the transducer 1202 performs a few specialized
transformations designed to systematize the tokenizing process. These
transformations can include, but are not limited to, the correction of
typographical errors, the expansion of contractions, the systematic
treatment of possessive, etc. In one embodiment of this transducer for
English, contractions such as don't are expanded to do not, and
possessives such as John's and nurses' are expanded to John 's and nurses
'. In one embodiment of this transducer for French, sequences such as
s'il, qu'avez, and j'adore are converted to pairs of tokens such as si il,
que avez, and je adore. In addition, a few thousand sequences such as afin
de are contracted to strings such as afin.sub.-- de. These sequences are
obtained from a list compiled by a native French speaker who felt that
such sequences should be treated as individual words. Also the four
strings au, aux, du, and des are expanded to a le, a les, de le, and de
les respectively.
Part-of-Speech Labeling Transducers (Brown et al. Section 3.2.5)
Referring again to FIG. 5, the transducer 1103 annotates words with
part-of-speech labels. These labels are used by the subsequent transducers
depicted the figure. In some embodiments of transducer 1103,
part-of-speech labels are assigned to a word sequence using a technique
based on hidden Markov models. A word sequence is assigned the most
probable part-of-speech sequence according to a statistical model, the
parameters of which are estimated from large annotated texts and other
even larger un-annotated texts. The technique is fully explained in
article by Bernard Merialdo entitled `Tagging text with a Probabilistic
Model` in the Proceedings of the International Conference on Acoustics,
Speech, and Signal Processing, May 14-17, 1991. This article is
incorporated by reference herein.
In an embodiment of the transducer 1103 for tagging of English, a tag set
consisting of 163 parts of speech is used. A rough categorization of these
parts of speech is given in Table 11.
In an embodiment of this transducer 1103 for the tagging of French, a tag
set consisting of 157 parts of speech is used. A rough categorization of
these parts of speech is given in Table 12.
TABLE 11
______________________________________
Parts of Speech for English
______________________________________
29 Nouns
27 Verbs
20 Pronouns
17 Determiners
16 Adverbs
12 Punctuation
10 Conjunctions
8 Adjectives
4 Prepositions
20 Other
______________________________________
TABLE 12
______________________________________
Parts-of-Speech for French
______________________________________
105 Pronouns
26 Verbs
18 Auxiliaries
12 Determiners
7 Nouns
4 Adjectives
4 Adverbs
4 Conjunctions
2 Prepositions
2 Punctuation
12 Other
______________________________________
Syntactic Transformation Transducers (Brown et al. Section 3.2.6)
Referring still to FIG. 5, the transducer 1104, which performs syntactic
transformations, will be described.
One function of this transducer is to simplify the subsequent morphological
transformations 1105. For example, in morphological transformations, verbs
may be analyzed into a morphological unit designating accidence followed
by another unit designating the root of the verb. Thus in French, the verb
ira might be replaced by 3s.sub.-- future.sub.-- indicative aller,
indicating that ira is the third person singular of the future tense of
aller. The same kind of transformation can be performed in English by
replacing the sequence will go by future.sub.-- indicative to.sub.-- go.
Unfortunately, often the two words in such a sequence are separated by
intervening words as in the sentences:
will he go play in the traffic?
he will not go to church.
Similarly, in French the third person of the English verb went is expressed
by the two words est alle, and these two words can be separated by
intervening words as in the sentences:
est-t-il alle?
Il n'est pas alle.
It is possible to analyze such verbs morphologically with simple string
replacement rules if various syntactic transformations that move away
intervening words are performed first.
A second function of the syntactic transducer 1104 is to make the task
presented to the statistical models which generate intermediate
target-structures for an intermediate source-structure as easy as
possible. This is done by performing transformations that make the forms
of these structures more similar. For example, suppose the source language
is English and the target language is French. English adjectives typically
precede the nouns they modify whereas French adjectives typically follow
them. To remove this difference, the syntactic transducer 1104 includes a
transducer which moves French words labeled as adjectives to positions
proceeding the nouns which they modify.
These transducers only deal with the most rudimentary linguistic phenomena.
Inadequacies and systematic problems with the transformations are overcome
by the statistical models used later in the target hypothesis-generating
module 702 of the invention. It should be understood that in other
embodiments of the invention more sophisticated schemes for syntactic
transformations with different functions can be used.
The syntactic transformations performed by the transducer 1104 are
performed in a series of steps. A sequence of words which has been
annotated with parts of speech is fed into the first transducer. This
transducer outputs a sequence of words and a sequence of parts of speech
which together serve as input to the second transducer in the series, and
so forth. The word and part-of-speech sequences produced by the final
transducer are the input to the morphological analysis transducer.
Syntactic Transducers for English (Brown et al. Section 3.2.7)
FIG. 7, depicts an embodiment of a syntactic transducer 1104 for English.
Although in much of this document, examples are described in which the
source language is French and the target language is English, here for
reasons of exposition, an example of a source transducer for a source
language of English is provided. In the next subsection, another example
in which the source language is French is provided. Those with a basic
knowledge of linguistics for other languages will be able to construct
similar syntactic transducer for those languages.
The syntactic transducer in FIG. 7 is comprised of three transducer that
perform question inversion 1301;
perform do-not coalescence 1302;
perform adverb movement 1303.
To understand the function of the transducer 1301 that performs question
inversion, note that in English questions the first auxiliary verb is
often separated by a noun phrase from the root of the verb as in the
sentences:
does that elephant eat?
which car is he driving?
The transducer 1301 inverts the auxiliary verb with the subject of the
sentence, and then converts the question mark to a special QINV marker to
signal that this inversion has occurred. For example, the two sentences
above are converted by this transducer to:
that elephant eats QINV
which car he is driving QINV
This transducer also removes supporting do's as illustrated in the first
sentence.
To understand the function of the transducer 1302 that performs do-not
coalescence, note that in English, negation requires the presence of an
auxiliary verb. When one doesn't exist, an inflection of to do is used.
The transducer 1302 coalesces the form of to do with not into the string
do.sub.-- not. For example,
##EQU7##
The part of speech assigned to the main verb, like above, is modified by
this transducer to record the tense and person of to do in the input. When
adverbs intervene in emphatic sentences the do.sub.-- not is positioned
after the intervening adverbs:
##EQU8##
To understand the function of the final transducer 1303 that performs
adverb movement, note that in English, adverbs often intervene between a
verb and its auxiliaries. The transducer 1303 moves adverbs which
intervene between a verb and its auxiliaries to positions following the
verb. The transducer appends a number onto the adverbs it moves to record
the positions from which they are moved. For example,
##EQU9##
An M2 is appended to both probably and to not to indicate that they
originally preceded the second word in the verbal sequence will be
balkanized. Similarly, an M3 is appended to completely to indicate that it
preceded the third word in the verbal sequence.
The transducer 1303 also moves adverbs that precede verbal sequences to
positions following those sequences:
##EQU10##
This is done in order to place verbs close to their subjects.
Syntactic Transducers for French (Brown et al. Section 3.2.8)
Referring now to FIG. 8, an embodiment of the syntactic transducer 1104 for
French is described. This embodiment comprises four transducers that
perform question inversion 1401;
perform discontinuous negative coalescence 1402;
perform pronoun movement 1403;
perform adverb and adjective movement 1404.
The question inversion transducer 1401 undoes French question inversion
much the same way that the transducer 1301 undoes English question
inversion:
##EQU11##
This transducer 1401 also modifies French est-ce que questions:
##EQU12##
To understand the function of the transducer 1402, which performs negative
coalescence, note that propositions in French are typically negated or
restricted by a pair of words, ne and some other word, that surround the
first word in a verbal sequence. The transducer 1402 moves the ne next to
its mate, and then coalesces the two into a new word:
##EQU13##
To understand the function of the transducer 1403, which performs pronoun
movement, note that in French, direct-object, indirect-object and
reflexive pronouns usually precede verbal sequences. The transducer 1403
moves these pronouns to positions following these sequences. It also maps
these pronouns to new words that reflect their roles as direct-object or
indirect-object, or reflexive pronouns. So, for example, in the following
sentence le is converted to le.sub.-- DPRO because it functions as a
direct object and vous to vous.sub.-- IPRO because it functions as an
indirect object:
##EQU14##
In the next sentence, vous is tagged as a reflexive pronoun and therefore
converted to vous.sub.-- RPRO.
##EQU15##
The allative pronominal clitic y and the ablative pronominal clitic en are
mapped to the two-word tuples a y.sub.-- PRO and de en.sub.-- PRO:
##EQU16##
The final transducer 1404 moves adverbs to positions following the verbal
sequences in which they occur. It also moves adjectives to positions
preceding the nouns they modify. This is a useful step in embodiments of
the present invention that translate from French to English, since
adjectives typically precede the noun in English.
Morphological Transducers (Brown et al. Section 3.2.9)
Referring again to FIG. 5, a transducer 1105, which performs morphological
transformations, will be described. One purpose of this transducer is to
make manifest in the intermediate source-structure representation the
fraternity of different forms of a word. This is useful because it allows
for more accurate statistical models of the translation process. For
example, a system that translates from French to English but does not use
a morphological transducer can not benefit from the fact that sentences in
which parle is translated as speaks provide evidence that parle should be
translated as spoken. As a result, parameter estimates for rare words are
inaccurate even when estimated from a very large training sample. For
example, even in a sample from the Canadian Parlement of nearly 30 million
words of French text, only 24 of the 35 different spellings of single-word
inflections of the verb parler actually occurred.
A morphological transducer 1104 is designed to ameliorate such problems.
The output of this transducer is a sequence of lexical morphemes. These
lexical morphemes will sometimes be referred to in this application as
morphological units or simply morphs. In an embodiment of transducer 1104
used for English, inflection morphological transformations are performed
that make evident common origins of different conjugations of the same
verb; the singular and plural forms of the same noun; and the comparative
and superlative forms of adjectives and adverbs. In an embodiment of
transducer 1104 used for French, morphological inflectional
transformations are performed that make manifest the relationship between
conjugations of the same verb; and forms of the same noun or adjective
differing in gender and number are performed. These morphological
transformations are reflected in the sequence of lexical morphemes
produced. The examples below illustrate the level of detail in these
embodiments of a morphological transducer 1104:
##EQU17##
Sense-Labelling Transducers (Brown et al. Section 3.2.10)
Referring again to FIG. 5, the transducer 1106, which annotates a lexical
morph sequence produced by the transducer 1105 with part-of-speech labels,
will be explained. Much of the allure of the statistical approach to
transfer in machine translation is the ability of that approach to
formally cope with the problem of lexical ambiguity. Unfortunately,
statistical methods are only able to mount a successful attack on this
problem when the key to disambiguating the translation of a word falls
within the local purview of the models used in transfer.
Consider, for example, the French word prendre. Although prendre is most
commonly translated as to take, it has a number of other less common
translations. A trigram model of English can be used to translate Je vais
prendre la decision as I will make the decision because the trigram make
the decision is much more common than the trigram take the decision.
However, a trigram model will not be of much use in translating Je vais
prendre ma propre decision as I will make may own decision because in this
case take and decision no longer fall within a single trigram.
In the paper, "Word Sense Disambiguation using Statistical Methods" in the
proceedings of the 29th Annual Meeting of the Association for
Computational Linguistics, published in June of 1991 by the Association of
Computational Linguistics and incorporated by reference herein, a
description is provided of a method of asking a question about the context
in which a word appears to assign that word a sense. The question is
constructed to have high mutual information with the translation of that
word in its context. By modifying the lexical entries that appear in a
sequence of morphemes to reflect the senses assigned to these entries,
informative global information can be encoded locally and thereby made
available to the statistical models used in transfer.
Although the method described in the aforementioned paper assigns senses to
words, the same method applies equally well to the problem of assigning
senses to morphemes, and is used here in that fashion. This transducer
1106, for example maps prendre to prendre.sub.-- 1 in the sentence
Je vais prendre ma propre voiture.
but to prendre.sub.-- 2 in the sentence
Je vais prendre ma propre decision.
It should be understood that other embodiments of the sense-labelling
transducer are possible. For example, the sense-labelling can be performed
by asking not just a single question about the context but a sequence of
questions arranged in a decision tree.
Source-Transducers with Constraints (Brown et al. Section 3.3)
In some embodiments, such as that depicted in FIG. 22, a source-structure
transducer, such as that labelled 901, accepts a set of constraints that
restricts its transformations source text to an intermediate target
structure in source text.
Such constraints include, but are not limited to,
requiring that a particular phrase be translated as a certain linguistic
component of a sentence, such a noun-phrase;
requiring that a source word be labelled as a certain part-of-speech such
as a verb or determiner;
requiring that a source word be morphologically analyzed a certain way;
requiring that a source word be annotated with a particular sense label;
in embodiments in which the intermediate structure encodes parse-tree or
case-frame information, requiring a certain parse or case-frame structure
for a sentence; morphologically analyzed in a particular way, or be
annotated with a particular sense-label.
A source-transducer accepting such constraints in similar to source
transducers as described in this section. Based on the description already
given, such transducers can be constructed by a person with a computer
science background and skilled in the art.
FINITE-STATE TRANSDUCERS (Brown et al. Section 4)
This section provides a description of an embodiment of a mechanism by
which the syntactic transductions in step 1104 and the morphological
transductions in step 1105 are performed. The mechanism is described in
the context of a particular example depicted in FIG. 9. One with a
background in computer science and skilled in the art of producing
finite-state transducers can understand from this example how to construct
syntactic and morphological transducers of the type described above.
The example transducer inverts questions involving do, does, and did. After
steps 1101, 1102, and 1103, the source text Why don't you ever succeed? is
transduced into parallel word and part-of-speech sequences 1501:
______________________________________
why do not you ever succeed ?
RRQ VDO XX PPY RR VVO ?
______________________________________
Here, RRQ and RR are adverb tags, VD0 and VV0 are verb tags, XX is a
special tag for the word not, PPY is a pronoun tag, and ? is a special tag
for a question mark.
The example transducer converts these two parallel sequences to the
parallel word and part-of-speech sequences 1502:
______________________________________
why you succeed do.sub.-- not.sub.-- MO
ever.sub.-- Mi
QINV
RRQ PPY VVO XX RR QINV
______________________________________
Here, QINV is a marker which records the fact that the original input
sentence was question inverted.
A mechanism by which a transducer achieves this transformation is depicted
in FIG. 10, and is comprised of four components:
a finite-state pattern matcher 1601;
an action processing module 1603;
a table of patterns 1602;
a table of action specifications 1503.
The transducer operates in the following way:
1. One or more parallel input sequences 1605 are captured by the
finite-state pattern-matcher 1601;
2. The finite-state pattern-matcher compare the input sequences against a
table of patterns 1602 of input sequences stored in memory;
3. A particular pattern is identified, and an associated action-code 1606
is transmitted to the action-processing module 1603;
4. The action-processing module obtains a specification of the
transformation associated to this action code from a table of actions 1503
stored in memory;
5. The action-processing module applies the transformation to the parallel
input streams to produce one or more parallel output sequences 1604.
The parallel input streams captured by the finite-state pattern matcher
1601 are arranged in a sequence of attribute tuples. An example of such a
sequence is the input sequence 1501 depicted in FIG. 9. This sequence
consists of a sequence of positions together with a set of one or more
attributes which take values at the positions. A few examples of such
attributes are the token attribute, the word attribute, the case-label
attribute, the part-of-speech attribute, the sense-label attribute. The
array of attributes for a given position will be called an attribute
tuple.
For example, the input attribute tuple sequence 1501 in FIG. 9 is seven
positions long and is made up of two dimensional attribute tuples. The
first component of an attribute tuple at a given position refers to the
word attribute. This attribute specifies the spellings of the words at
given positions. For example, the first word in the sequence 1501 is why.
The second component of an attribute tuple at a given position for this
input sequence refers to a part-of-speech tag for that position. For
example, the part of speech at the first position is RRQ. The attribute
tuple at position 1 is thus the ordered pair why, RRQ.
The parallel output streams produced by the action processing module 1603
are also arranged as a sequence of attribute tuples. The number of
positions in an output sequence may be different from the number of
positions in an input sequence.
For example, the output sequence 1502 in FIG. 9, consists of six positions.
Associated with each position is a two-dimensional attribute tuple, the
first coordinate of which is a word attribute and the second coordinate of
which is a part-of-speech attribute.
An example of a table of patterns 1602 is shown in FIG. 11. This table is
logically divided into a number of parts or blocks.
Pattern-Action Blocks. The basic definitions of the matches to be made and
the actions to be taken are contained in pattern-action blocks. A
pattern-action block comprises of a list of patterns together with the
name of actions to be invoked when patterns in an input attribute-tuple
sequence 1605 are matched.
Auxiliary Pattern Blocks. Patterns that can be used as sub-patterns in the
patterns of pattern-action blocks are defined in Auxiliary Pattern blocks.
Such blocks contain lists of labelled patterns of of attributes tuples.
These labelled patterns do not have associated actions, but can be
referenced by their name in the definitions of other patterns.
In FIG. 11 there is one Auxiliary Pattern block. This block defines four
auxiliary patterns. The first of these has a name ADVERB and matches
single tuple adverb-type constructions. The second has a name of
BARE.sub.-- NP and matches certain noun-phrase-type constructions. Notice
that this auxiliary pattern makes use of the ADVERB pattern in its
definition. The third and fourth auxiliary patterns match other types of
noun phrases.
Set Blocks. Primary and auxiliary patterns allow for sets of attributes. In
FIG. 11, for example, there is a set called DO.sub.-- SET, of various
forms of to.sub.-- do, and another set PROPER.sub.-- NOUN.sub.-- TAG of
proper-noun tags.
Patterns are defined in terms of regular expressions of attribute tuples.
Any pattern of attribute tuples that can be recognized by a deterministic
finite-state automata can be specified by a regular expression. The
language of regular expressions and methods for constructing finite-state
automata are well known to those skilled in computer science. A method for
constructing a finite-state pattern matcher from a set of regular
expressions is described in the article "LEX--A Lexical Analyzer
Generator," written by Michael E. Lesk, and appearing in the Bell Systems
Technical Journal, Computer Science Technical Report Number 39, published
in October of 1975.
Regular expressions accepted by the pattern matcher 1601 are described
below.
Regular Expressions of Attribute Tuples: A regular expression of attribute
tuples is a sequence whose elements are either
1. an attribute tuple;
2. the name of an auxiliary regular expression; or
3. a register name.
These elements can be combined using one of the logical operations:
______________________________________
Operator Meaning Usage Matches
______________________________________
. concatenation
A.B A followed by B
.vertline.
union (i.e. or)
A.vertline.B
A or B
* 0 or more A* 0 or more A's
? 0 or 1 A? 0 or 1 A's
+ 1 or more A+ 1 or more A's
______________________________________
Here A and B denote other regular expressions.
Examples of these logical operations are:
______________________________________
Expression Matches
______________________________________
A?.B.C 0 or 1 A's followed by B then by C
(A*).vertline.(B+)
0 or more A's or 1 or more B's
(A.vertline.B).C
A or B, followed by C
______________________________________
Attribute Tuples: The most common type of element in a regular expression
is an attribute tuple. An attribute tuple is a vector whose components are
either
1. an attribute (as identified by its spelling);
2. a name of a set of attributes;
3. the wild card attribute.
These elements are combined using the following operators:
______________________________________
Operator Meaning Usage
______________________________________
, Delimiter between coordinates
a,b
of an attribute tuple
Negation a
# Wild Card #
______________________________________
(Here a and b denote attribute spellings or attribute set names).
The meanings of these operators are best illustrated by example. Let a, b,
and c denote either attribute spellings or set names. Assume the dimension
of the attribute tuples is 3. Then:
______________________________________
Attribute Tuple
Matches
______________________________________
a,b,c First attribute matches a, second match b,
third matches c
,b,c First attribute elided (matches anything),
Second attribute matches b, third matches c
,b, First and third attribute elided (match anything)
Second attribute matches b
a Second and third attributes elided (Match
anything) First matches a
#,b, First attribute wild-card (i.e matches anything)
Second attribute matches b. Third attribute
elided
a, b, c Second attribute matches anything EXCEPT b.
Third matches anything EXCEPT c.
______________________________________
Auxiliary Regular Expressions: A second type of element in a regular
expression is an auxiliary regular expression. An auxiliary regular
expression is a labelled regular expression which is used as a component
of a larger regular expression.
Logically, a regular expression involving auxiliary regular expressions is
equivalent to the regular expression obtained by resolving the reference
to the auxiliary pattern. For example, suppose an auxiliary regular
expression named D has been defined by:
D=A.B+.A*
where A,B denote attribute tuples (or other auxiliary patterns). Then:
______________________________________
Expression is equivalent to
______________________________________
C.D C.A.B+.A*
D+.C.D (A.B+.A*)+.A.B+.A*.C.A*.B+.A*
______________________________________
Registers: Just knowing that a regular expression matches an input
attribute tuple sequence usually does not provide enough information for
the construction of an appropriate output attribute tuple sequence. Data
is usually also required about the attribute tuples matched by different
elements of the regular expression. In ordinary LEX, to extract this type
of information often requires the matched input sequence to be parsed
again. To avoid this cumbersome approach, the pattern-matcher 1601 makes
details about the positions in the input stream of the matched elements of
the regular expression more directly available. From these positions, the
identities of the attribute tuples can then be determined.
Positional information is made available through the use of registers. A
register in a regular expression does not match any input. Rather,
1. After a match, a register is set equal to the position in the input
sequence of the next tuple in the input sequence that is matched by an
element of the regular expression to the right of the register.
2. If no further part of the regular expression to the right of the
register matches, then the register is set equal to zero.
The operation of registers is best illustrated by some examples. These
examples use registers 1! and 2!:
______________________________________
Expression
Contents of Registers after match
______________________________________
A. 1!.B.C
Reg 1: First position of B match
A. 2!.(C.vertline.D)
Reg 2: First position of either C or D match
A. 1!.B*. 2!.C
Reg 1: If B matches:
First position of B match
Otherwise: First position of C match
Reg 2:
First position of C match
A. 1!.B*.C*
Reg 1: If B matches:
First position of B match
If C matches:
First position of C match
Otherwise: 0
Reg 2: If C matches:
First position of C match
Otherwise: 0
______________________________________
A pattern-action block defines a pattern matcher. When an input
attribute-tuple sequence is presented to the finite-state pattern matcher
a current input position counter is initialized to 1 denoting that the
current input position is the first position of the sequence. A match at
the current input position is attempted for each pattern. If no pattern
matches, an error occurs. If more than one pattern matches, the match of
the longest length is selected. If several patterns match of the same
longest length, the one appearing first in the definition of the
pattern-action block is selected. The action code associated with that
pattern is then transmitted to the action processing module 1603.
Transformations by the action processing module are defined in a table of
actions 1503 which is stored in memory. The actions can be specified in
specified in any one of a number of programming languages such as C,
PASCAL, FORTRAN, or the like.
In the question-inversion example, the action specified in the pseudo-code
in FIG. 18 is invoked when the pattern defined by the regular expression
in lines 3-4 is matched. This action inverts the order of the words in
certain questions involving forms of do. An instance of this action is
shown in FIG. 9. In the pseudo-code of FIG. 12 for this action, the symbol
@reg(i) denotes the contents of register i. In line 6 of this pseudo-code,
the output attribute tuple sequence is set to null.
A question matched by the regular expression in lines 3-4 may or may not
begin with a (so-called) wh- word in the set WH.sub.-- NP. If it does
match, the appropriate action is to append the input tuple in the first
position to the output sequence. This is done in lines 8-9.
After the wh-word, the next words of the output sequence should be the
subject noun phrase of the input sequence. This is made so in line 11-12
that appends all tuples matching the regular expression SUBJECT.sub.-- NP
to the output sequence.
For negative questions involving forms of do, the part-of-speech tag of the
output verb and of the output adverbs are the same as those of the input
verb and adverbs. Thus the entire input tuple sequences corresponding to
these words can be appended to the output. This is done in lines 15-18.
For positive questions the tag attribute of the output verb may be
different than that of the input verb. This is handled in lines 25-37. The
input word attribute for the verb is appended to the output word attribute
in lines 26 and 31 and 35. The output tag attribute is selected based on
the form of do in the input sequence. Explicit tag values are appended to
the output sequence in lines 32 and 37.
The remaining input words and tags other than the question mark are written
to the output sequence in lines 43-44. The input sequence is completed in
line 46 by the marker QINV signalling question inversion, together with
the appropriate tag.
SENSE DISAMBIGUATION (Brown et al. Section 11)
Introduction (Brown et al. Section 11.1)
An alluring aspect of the statistical approach to machine translation is
the systematic framework it provides for attacking the problem of lexical
disambiguation. For example, an embodiment of the machine translation
system depicted in FIG. 50 translates the French sentence Je vais prendre
la decision as I will make the decision, correctly interpreting prendre as
make. Its statistical translation model, which supplies English
translations of French words, prefers the more common translation take,
but its trigram language model recognizes that the three-word sequence
make the decision is much more probable than take the decision.
This system is not always so successful. It incorrectly renders Je vais
prendre ma propre decision as I will take may own decision. Its language
model does not realize that take my own decision is improbable because
take and decision no longer fall within a single trigram.
Errors such as this are common because the statistical models of this
system only capture local phenomena; if the context necessary to determine
a translation falls outside the scope of these models, a word is likely to
be translated incorrectly. However, if the relevant context is encoded
locally, a word can be translated correctly.
As has been noted in Section 3, such encoding can be performed in the
source-transduction phase 701 by a sense-labeling transducer.
In this section, the operation and construction of such a sense-labeling
transducer is described. The goal of this transducer is to perform
cross-lingual word-sense labeling. That is, this transducer labels words
of a sentence in a source language so as to elucidate their translations
into a target language. Such a transducer can also be used to label the
words of an target sentence so as to elucidate their translations into a
source language.
The design of this transducer is motivated by some examples. In some
contexts the French verb prendre translates into English as to take, but
in other contexts it translates as to make. A sense disambiguation
transformation, by examining the contexts, might label occurrences of
prendre that likely mean to take with one label, and other occurrences of
prendre with another label. Then the uncertainty in the translation of
prendre given the label would be less than the uncertainty in the
translation of prendre without the label. Although the label does not
provide any information that is not already present in the context, it
encodes this information locally. Thus a local statistical model for the
transfer of labeled sentences should be more accurate than one for the
transfer of unlabeled ones.
While the translation of a word depends on many words in its context, it is
often possible to obtain information by looking at only a single word. For
example, in the sentence Je vais prendre ma propre decision (I will make
may own decision), the verb prendre should be translated as make because
its object is decision. If decision is replaced by voiture then prendre
should be translated as take: Je vais prendre ma propre voiture (I will
take my own car). Thus the uncertainty in the translation of prendre is
reduced by asking a question about its object, which is often the first
noun to its right. A sense can be assigned to prendre based upon the
answer to this question.
As another example, in Il doute que les notres gagnent (He doubts that we
will win), the word il should be translated as he. On the other hand, if
doute is replaced by faut then il should be translated as it: Il faut que
les notres gagnent (It is necessary that we win). Here, a sense label can
be assigned to il by asking about the identity of the first verb to its
right.
These examples motivate a sense-labeling scheme in which the label of a
word is determined by a question about an informant word in its context.
In the first example, the informant of prendre is the first noun to the
right; in the second example, the informant of il is the first verb to the
right. The first example is depicted in FIG. 14. The two sequence of this
example are labeled 4001 and 4002 in the figure.
If more than two senses are desired for a word, then questions with more
than two answers can be considered.
Design of a Sense-Labeling Transducer (Brown et al. Section 11)
FIG. 15 depicts an embodiment of a sense-labeling transducer based on this
scheme. For expositional purposes, this embodiment will be discussed in
the context of labeling words in a source language word sequence. It
should be understood that in other embodiments, a sense-labeling
transducer can accept as input more involved source-structure
representations, including but not limited to, lexical morpheme sequences.
In these embodiments, the constituents of a representation of a source
word sequence are annotated by the sense-labeling transducer with labels
that elucidate their translation into analogous constituents into a
similar representation of a target word sequence. It should also be
understood that in still other embodiments, a sense-labeling transducer
annotates, target-structure representations, (not source-structure
representations) with sense labels.
The operation of the embodiment of the sense-labeling transducer depicted
in FIG. 15 comprises the steps of:
4101. Capturing an input sequence consisting of a sequence of words in a
source language;
4102. For each word in the input sequence performing the Steps 4107, 4108,
4109, 4104 until no more words are available in Step 4105;
4107. For the input word being considered, finding a best informant site
such as the noun to the right, the verb to the left, etc. A best informant
for a word is obtained using a table 4103 stored in memory of informants
and questions about the informants for every word in the source language
vocabulary;
4108. Finding the word at the best informant site in the input word
sequence;
4109. Obtaining the class of the informant word as given by the answer to a
question about the informant word;
4104. Labeling the original input word of the input sequence with the class
of the informant word.
For the example depicted in FIG. 14, the informant site determined by Step
4107 is the noun to the right. For the first word sequence 4001 of this
example, the informant word determined by Step 4108 is decision; for the
second word sequence 4109, the informant word is voiture. In this example,
the class of decision determined in Step 4109 is different than the class
of voiture. Thus the label attached to prendre by Step 4104 is different
for these two contexts of prendre.
Constructing a Table of Informants and Questions (Brown et al. Section
11.3)
An important component of the sense-labeler depicted in FIG. 15 is a table
4103 of informants and questions for every word in a source language
vocabulary.
FIG. 16 depicts a method of constructing such a table. This method
comprises the steps of:
4203. Performing the Steps 4201 and 4202 for each word in a source language
vocabulary.
4201. For the word being considered, finding a good question for each of a
plurality of informant sites. These informant sites are obtained from a
table 4207 stored in memory of possible informant sites. Possible sites
include but are not limited to, the nouns to the right and left, the verbs
to the right and left, the words to the right and left, the words two
positions to the right or left, etc. A method of finding a good question
is described below. This method makes use of a table 4205 stored in memory
probabilities derived from Viterbi alignments. These probabilities are
also discussed below.
4202. Storing in a table 4208 of informants and questions, the informant
site and the good question.
A method for carrying out the Step 4201 for finding a good question for
each informant site of a vocabulary word is depicted in FIG. 17. This
method comprises the steps of:
4301. Performing the Steps 4302 and 4207 for each possible informant site.
Possible informant sites are obtained from a table 4304 of such sites.
4302. For the informant site being considered, finding a question about
informant words at this site that provides a lot of information about
translations of the vocabulary word into the target language.
4207. Storing the vocabulary word, the informant site, and the good
question in a table 4103.
Mathematics of Constructing Questions (Brown et al. Section 11.4)
A method for carrying out the Step 4302 of finding a question about an
informant is depicted in FIG. 18. In this subsection, the some
preliminaries for describing this method are given. The notation used here
is the same as that used in Sections 8-10.
Statistical Translation with Transductions (Brown et al. Section 11.4.1)
Recall the setup depicted in FIG. 19. A system shown there employs
1. A source transducer 201 which encodes a source sentence f into an
intermediate structure f'.
2. A statistical translation component 202 which translates f' into a
corresponding intermediate target structure e'. This component
incorporates a language model, a translation model, and a decoder.
3. A target transducer 203 which reconstructs a target sentence e from e'.
For statistical modeling, the target-transduction transformation 203
e'.fwdarw.e is sometimes required to be invertible. Then e' can be
constructed from e and no information is lost in the transformation.
The purpose of source and target transduction is to facilitate the task of
the statistical translation. This will be accomplished if the probability
distribution Pr(f',e') is easier to model then the original distribution
Pr(f,e). In practice this means that e' and f' should encode global
linguistic facts about e and f in a local form.
A useful gauge of the success that a statistical model enjoys in modeling
translation from sequences of source words represented by a random
variable F, to sequences of target words represented by a random variable
E, is the cross entropy.sup.3
.sup.3 In this equation and in the remainder of this section, the
convention of using uppercase letters (e.g. E) for random variables and
lower case letters (e.g. e) for the values of random variables continues
to be used.
##EQU18##
The cross entropy measures the average uncertainty that the model has about
the target language translation e of a source language sequence f. Here
P(e.vertline.f) is the probability according to the model that e is a
translation of f and the sum runs over a collection of all S pairs of
sentences in a large corpus comprised of pairs of sentences with each pair
consisting of a source and target sentence which are translations of one
another (See Sections 8-10).
A better model has less uncertainty and thus a lower cross entropy. The
utility of source and target transductions can be measured in terms of
this cross entropy. Thus transformations f.fwdarw.f' and e'.fwdarw.e are
useful if models P'(f'.vertline.e') and P'(e') can be constructed such
that H(E'.vertline.F')<H(E.vertline.F).
Sense-Labeling in Statistical Translation (Brown et al. Section 11.4.2)
The remainder of this section is devoted to describing methods for
constructing a sense-labeling transducer. In this case the following setup
prevails:
The Intermediate Structures. Intermediate structures e' and f' consist of
sequences of words labeled by their senses. Thus f' is a sentence over the
expanded vocabulary whose `words` f' are pairs (f,s) where f is a word in
the original source vocabulary and s is its sense label. Similarly, e' is
a sentence over the expanded vocabulary whose words e' are pairs (e,s)
where e is a target word and s is its sense label.
Source and target transductions. For each source word and each target word,
an informant site, such as first noun to the left is chosen, and an n-ary
question about the value of the informant at that site is also chosen. A
source-transduction transformation f.fwdarw.f' and an inverse
target-transduction transformation e.fwdarw.e' map a sentence to the
intermediate structure in which each word is labeled by a sense,
determined by the question about its informant. A target-transduction
transformation e'.fwdarw.e maps a labeled sentence to a sentence in which
the labels have been removed.
The probability models. A translation model such as one of the models in
Sections 8-10 is used for both P'(F'.vertline.E') and for P(F.vertline.E).
A trigram language model such as that discussed in Section 6 is used for
both P(E) and P'(E').
The Viterbi Approximation (Brown et al. Section 11.4.3)
The probability P(f.vertline.e) computed by the translation model requires
a sum over alignments as discussed in detail in Sections 8-10. This sum is
often too expensive to compute directly since the number of alignments
increases exponentially with sentence length. In the mathematical
considerations of this Section, this sum will be approximated by the
single term corresponding to the alignment .nu.(f.vertline.e), with
greatest probability. This is the Viterbi approximation already discussed
in Sections 8-10 and .nu.(f.vertline.e) is the Viterbi alignment.
Let c(f.vertline.e) be the expected number of times that e is aligned with
f in the Viterbi alignment of a pair of sentences drawn at random from a
large corpus of training data. Let c(.phi..vertline.e) be the expected
number of times that e is aligned with .phi. words. Then
##EQU19##
where c(f.vertline.e;.nu.) is the number of times that e is aligned with f
in the alignment A, and c(.phi..vertline.e;.nu.) is the number of times
that e generates .phi. target words in A. The counts above are also
expressible as averages with respect to the model:
##EQU20##
Probability distributions p(e,f) and p(.phi.,e) are obtained by normalizing
the counts c(f.vertline.e) and c(.phi..vertline.e):
##EQU21##
.sup.4 In these equations and in the remainder of the paper, the generic
symbol
##EQU22##
is used to denote a normalizing factor that converts counts to
probabilities. The actual value of
##EQU23##
will be implicit from the context. Thus, for example, in the left hand
equation of (168), the normalizing factor is norm = .SIGMA..sub.f,e
c(f.vertline.e) which equals the average length of source sentences. In
the right hand equation of (168), the normalizing factor is the average
length of target sentences.
(These are the probabilities that are stored in a table of probabilities
4205.) The conditional distributions p(f.vertline.e) and
p(.phi..vertline.e) are the Viterbi approximation estimates for the
parameters of the model. The marginals satisfy
##EQU24##
where u(e) and u(f) are the unigram distributions of e and f and
.phi.(e)=.SIGMA..sub..phi. p(.phi..vertline.e).phi. is the average number
of source words aligned with e. These formulae reflect the fact that in
any alignment each source word is aligned with exactly one target word.
Cross Entropy (Brown et al. Section 11.4.4)
In this subsection the cross entropies H(E.vertline.F) and
H(E'.vertline.F') are expressed in terms of the information between source
and target words.
In the Viterbi approximation, the cross entropy H(F.vertline.E) is given by
H(F.vertline.E)=m{H(E.vertline.F)+H(.PHI..vertline.E)}, (170)
where m is the average length of the source sentences in the training data,
and H(F.vertline.E) and H(.PHI..vertline.E) are the conditional entropies
for the probability distributions p(f,e) and p(.phi.,e):
##EQU25##
A similar expression for the cross entropy H(E.vertline.F) will now be
given. Since
P(f,e)=P(f.vertline.e)P(e),
this cross entropy depends on both the translation model, P(f.vertline.e),
and the language model, P(e). With a suitable additional approximation,
H(E.vertline.F)=m{H(.PHI..vertline.E)-I(E,F)},+H(E) (172)
where H(E) is the cross entropy of P(E) and I(F,E) is the mutual
information between f and e for the probability distribution p(f,e).
The additional approximation required is,
##EQU26##
where p(f) is the marginal of p(f,e). This amount to approximating P(f) by
the unigram distribution that is closest to it in cross entropy. Granting
this, formula (172) is a consequence of (170) and of the identities
H(E.vertline.F)=H(E.vertline.F)-H(F)+H(E),
H(F)=H(F.vertline.E)+I(F,E). (174)
Next consider H(E'.vertline.F'). Let e.fwdarw.e' and f.fwdarw.f' be sense
labeling transformations of the type discussed above. Assume that these
transformations preserve Viterbi alignments; that is, if the words e and f
are aligned in the Viterbi alignment for (f,e), then their sensed versions
e' and f' are aligned in the Viterbi alignment for (f',e'). It follows
that the word translation probabilities obtained from the Viterbi
alignment satisfy p(f,e)=.SIGMA..sub.f'.epsilon.f
p(f',e)=.SIGMA..sub.e'.epsilon.e p(f,e'), where the sums range over the
sensed versions f' of f and the sensed versions e' of e.
By applying (172) to the cross entropies H(E.vertline.F), H(E.vertline.F'),
and H(E'.vertline.F), it is not hard to verify that
##EQU27##
Here I(E,F'.vertline.f) is the conditional mutual information given a
source word f between its translations E and its sensed versions F';
I(F,E'.vertline.e) is the conditional mutual information given a target
word e between its translations F and its sensed versions E'; and I(.PHI.,
E'.vertline.e) is the conditional mutual information given e between .PHI.
and its sensed versions E'.
Selecting Questions (Brown et al. Section 11.5)
The method depicted in FIG. 18 for finding good informants and questions
for sensing is now described.
Source Questions (Brown et al. Section 11.5.1)
For sensing source sentences, a question about an informant is a function c
from the source vocabulary into the set of possible senses. If the
informant of f is x, then f is assigned the sense c(x). The function c(x)
is chosen to minimize the cross entropy H(E.vertline.F'). From formula
(175), this is equivalent to maximizing the conditional mutual information
I(F',E.vertline.f) between E and F'
##EQU28##
where p(f,e,x) is the probability distribution obtained by counting the
number of times in the Viterbi alignments that e is aligned with f and the
value of the informant of f is x,
##EQU29##
An exhaustive search for the best c requires a computation that is
exponential in the number of values of x and is not practical. In the
aforementioned paper entitled "Word-Sense Disambiguation using Statistical
Methods" by P. F. Brown, et al., a good c is found using the flip-flop
method which is only applicable if the number of senses is restricted to
two. Here a different method that can be used to find c for any number of
senses is described. This method uses the technique of alternating
minimization, and is similar to the k-means method for determining pattern
clusters and to the generalized Lloyd method for designing vector
quantitizers.
The method is based on the fact that, up to a constant independent of c,
the mutual information I(F',E.vertline.f) can be expressed as an infimum
over conditional probability distributions q(E.vertline.c),
##EQU30##
The best value of the information is thus an infimum over both the choice
for c and the choice for the q. This suggests the iterative method,
depicted in 4401 for obtaining a good c. This method comprises the steps
of:
4401. Beginning with an initial choice of c;
4404. Performing Steps 4402 and 4403 until no further increase in
I(F',E.vertline.f) results;
4403. For given q, finding the best c:
c(x)=argmin.sub.c D(p(E.vertline.x,f); q(E.vertline.c));
4402. For this c, finding the best q:
##EQU31##
Target Questions (Brown et al. Section 11.5.2)
For sensing target sentences, a question about an informant is a function c
from the target vocabulary into the set of possible senses. c is chosen to
minimize the entropy H(E'.vertline.F). From (175) this is equivalent to
maximizing the sum
I(F,E'.vertline.e)+I(.PHI.,E'.vertline.e).
In analogy to (179),
##EQU32##
Again a good c is obtained alternating minimization.
Generalizations (Brown et al. Section 11.6)
The method of sense-labeling discussed above ask a single question about a
single word of context. In other embodiments of the sense labeler, this
question is the first question in a decision tree. In still other
embodiments, rather than using a single informant site to determine the
sense of a word, questions from several different informant sites are
combined to determine the sense of a word. In one embodiment, this is done
by assuming that the probability of an informant word x.sub.i at informant
site i, given a target word e, is independent of an informant word x.sub.j
at a different informant site j given the target word e. Also, in other
embodiments, the intermediate source and target structure representations
are more sophisticated than word sequences, including, but not limited to,
sequences of lexical morphemes, case frame sequences, and parse tree
structures.
Table 1 shows a hypothetical example of an input series of source words
according to the invention. In this example, the source words are French
words.
TABLE 1
______________________________________
Input Series of Source Words, F
f.sub.1 f.sub.2
f.sub.3 f.sub.4
f.sub.5
f.sub.6
______________________________________
La clef est dans la porte
.
______________________________________
The translation apparatus according to the present invention further
comprises a target hypothesis generator 12. The target hypothesis
generator 12 generates at least two target hypotheses. Each target
hypothesis comprises a series of target words selected from a vocabulary
of words in the second language. The vocabulary of words in the second
language may be stored in a target language vocabulary store 14. Each
target word in a target hypothesis has a context comprising at least one
other word in the target hypothesis.
An example of a target hypothesis generator is described in Section 14 of
Brown et al, cited above, which is incorporated herein by reference, and
set forth in full herein at this time.
HYPOTHESIS SEARCH--STEPS 702 AND 902 (Brown et al. Section 14)
Overview of Hypothesis Search (Brown et al. Section 14.1)
Referring now to FIG. 20, the second step 702 produces a set of
hypothesized target structures which correspond to putative translations
of the input intermediate source structure produced by step 701. The
process by which these target structures are produced is referred to as
hypothesis search. In a preferred embodiment target structures correspond
to sequences of morphemes. In other embodiments more sophisticated
linguistic structures such as parse trees or case frames may be
hypothesized.
An hypothesis in this step 702 is comprised of a target structure and an
alignment of a target structure with the input source structure.
Associated with each hypothesis is a score. In other embodiments a
hypothesis may have multiple alignments. In embodiments in which step 701
produces multiple source structures an hypothesis may contain multiple
alignments for each source structure. It will be assumed here that the
target structure comprised by a hypothesis contains a single instance of
the null target morpheme. The null morphemes will not be shown in the
figures pertaining to hypothesis search, but should be understood to be
part of the target structures nonetheless. Throughout this section on
hypothesis search, partial hypothesis will be used interchangeably with
hypothesis, partial alignment with alignment, and partial target structure
with target structure.
The target structures generated in this step 702 are produced
incrementally. The process by which this is done is depicted in FIG. 21.
This process is comprised of five steps.
A set of partial hypotheses is initialized in step 5401. A partial
hypothesis is comprised of a target structure and an alignment with some
subset of the morphemes in the source structure to be translated. The
initial set generated by step 5401 consists of a single partial
hypothesis. The partial target structure for this partial hypothesis is
just an empty sequence of morphemes. The alignment is the empty alignment
in which no morphemes in the source structure to be translated are
accounted for.
The system then enters a loop through steps 5402, 5403, and 5404, in which
partial hypotheses are iteratively extended until a test for completion is
satisfied in step 5403. At the beginning of this loop, in step 5402, the
existing set of partial hypotheses is examined and a subset of these
hypotheses is selected to be extended in the steps which comprise the
remainder of the loop. In step 5402 the score for each partial hypothesis
is compared to a threshold (the method used to compute these thresholds is
described below). Those partial hypotheses with scores greater than
threshold are then placed on a list of partial hypotheses to be extended
in step 5404. Each partial hypothesis that is extended in step 5404
contains an alignment which accounts for a subset of the morphemes in the
source sentence. The remainder of the morphemes must still be accounted
for. Each extension of an hypothesis in step 5404 accounts for one
additional morpheme. Typically, there are many tens or hundreds of
extensions considered for each partial hypothesis to be extended. For each
extension a new score is computed. This score contains a contribution from
the language model as well as a contribution from the translation model.
The language model score is a measure of the plausibility a priori of the
target structure associated with the extension. The translation model
score is a measure of the plausibility of the partial alignment associated
with the extension. A partial hypothesis is considered to be a full
hypothesis when it accounts for the entire source structure to be
translated. A full hypothesis contains an alignment in which every
morpheme in the source structure is aligned with a morpheme in the
hypothesized target structure. The iterative process of extending partial
hypotheses terminates when step 5402 produces an empty list of hypotheses
to be extended. A test for this situation is made on step 5403.
This method for generating target structure hypotheses can be extended to
an embodiment of step 902 of FIG. 22, by modifying the hypothesis
extension step 5404 in FIG. 21, with a very similar step that only
considers extensions which are consistent with the set of constraints 906.
Such a modification is a simple matter for one skilled in the art.
Hypothesis Extension 5404 (Brown et al. Section 14.2)
This section provides a description of the method by which hypotheses are
extended in step 5404 of FIG. 21. Examples will be taken from an
embodiment in which the source language is French and the target language
is English. It should be understood however that the method described
applies to other language pairs.
Types of Hypothesis Extension (Brown et al. Section 14.2.1)
There are a number of different ways a partial hypothesis may be extended.
Each type of extension is described by working through an appropriate
example. For reasons of exposition, the method described in this section
assigns scores to partial hypotheses based on Model 3 from the section
entitled Translation Models and Parameter Estimation. One skilled in the
art will be able to adopt the method described here to other models, such
as Model 5, which is used in the best mode of operation.
A partial hypothesis is extended by accounting for one additional
previously unaccounted for element of the source structure. When a partial
hypothesis H.sub.1 is extended to some other hypothesis H.sub.2, the score
assigned to H.sub.2 is a product of the score associated with H.sub.1 and
a quantity denoted as the extension score. The value of the extension
score is determined by the language model, the translation model, the
hypothesis being extended and the particular extension that is made. A
number of different types of extensions are possible and are scored
differently. The possible extension types and the manner in which they are
scored is illustrated in the examples below.
As depicted in FIG. 23, in a preferred embodiment, the French sentence La
jeune fille a reveille sa mere is transduced in either step 701 of FIG. 20
or step 901 of FIG. 22 into the morphological sequence la jeune fille
V.sub.-- past.sub.-- 3s sa mere.
The initial hypothesis accounts for no French morphemes and the score of
this hypothesis is set to 1. This hypothesis can be extended in a number
of ways. Two sample extensions are shown in FIGS. 24 and 49. In the first
example in FIG. 24, the English morpheme the is hypothesized as accounting
for the French morpheme la. The compound of the score associated with this
extension is the equal to
l(the.vertline.*,*)n(1.vertline.the)t(la.vertline.the)d(1.vertline.1).(182)
Here, * is a symbol which denotes a sequence boundary, and the factor
l(the.vertline.*,*) is the trigram language model parameter that serves as
an estimate of the probability that the English morpheme the occurs at the
beginning of a sentence. The factor n(1.vertline.the) is the translation
model parameter that is an estimate of the probability that the English
morpheme the has fertility 1, in other words, that the English morpheme
the is aligned with only a single French morpheme. The factor
t(la.vertline.the) is the translation model parameter that serves as an
estimate of the lexial probability that the English morpheme the
translates to the French morpheme la. Finally, the factor d(1.vertline.1)
is the translation model parameter that serves as an estimate of the
distortion probability that a French morpheme will be placed in position 1
of the French structure given that it is aligned with an English morpheme
that is in position 1 of the English structure. In the second example in
FIG. 49, the English morpheme mother is hypothesized as accounting for the
French morpheme mere. The score for this partial hypothesis is
l(mother.vertline.*,*)n(1.vertline.mother)t(mere.vertline.mother)d(7.vertli
ne.1). (183)
Here, the final factor d(7.vertline.1) serves as an estimate of the
distortion probability that a French morpheme, such as mere, will be place
in the 7th position in a source sequence given that it is aligned with an
English morpheme such as mother which is in the 1st position in an
hypothesized target sequence.
Now, suppose the partial hypothesis in FIG. 24 is to be extended on some
other invocation of step 5404. A common translation of the pair of French
morphemes jeune fille is the English morpheme girl. However, since in a
preferred embodiment a partial hypothesis is extended to account for only
a single French morpheme at a time, it is not possible to account for both
jeune and fille with a single extension. Rather the system first accounts
for one of the morphemes, and then on another round of extensions,
accounts for the other. This can be done in two ways, either by accounting
first for jeune or by accounting first for fille. FIG. 25 depicts the
extension that accounts first for fille. The + symbol in FIG. 25 after the
the English morpheme girl denotes the fact that in these extensions girl
is to be aligned with more French morphemes than it is currently aligned
with, in this case, at least two. A morpheme so marked is referred to as
open. A morpheme that is not open is said to be closed. A partial
hypothesis which contains an open target morpheme is referred to as open,
or as an open partial hypothesis. A partial hypothesis which is not open
is referred to as closed, or as a closed partial hypothesis. An extension
is referred to as either open or closed according to whether or not the
resultant partial hypothesis is open or closed. In a preferred embodiment,
only the last morpheme in a partial hypothesis can be designated open. The
score for the extension in FIG. 25 is
##EQU33##
Here, the factor l(girl.vertline.*,the) is the language model parameter
that serves as an estimate of the probability with which the English
morpheme girl is the second morpheme in a source structure in which the
first morpheme is the. The next factor of 2 is the combinatorial factor
that is discussed in the section entitled Translation Models and Parameter
Estimation. It is factored in, in this case, because the open English
morpheme girl is to be aligned with at least two French morphemes. The
factor n(i.vertline.girl) is the translation model parameter that serves
as an estimate of the probability that the English morpheme girl will be
aligned with exactly i French morphemes, and the sum of these parameters
for i between 2 and 25 is an estimate of the probability that girl will be
aligned with at least 2 morphemes. It is assumed that the probability that
an English morpheme will be aligned with more than 25 French morphemes is
0. Note that in a preferred embodiment of the present invention, this sum
can be precomputed and stored in memory as a separate parameter. The
factor t(fille.vertline.girl) is the translation model parameter that
serves as an estimate of the lexical probability that one of the French
morphemes aligned with the English morpheme girl will be the French
morpheme fille. Finally, the factor d(3.vertline.2) is the translation
model parameter that serves as an estimate of the distortion probability
that a French morpheme will be placed in position 3 of the French
structure given that it is aligned with an English morpheme which is in
position 2 of the English structure. This extension score in Equation 184
is multiplied by the score in Equation 182 for the partial hypothesis
which is being extended to yield a new score for the partial hypothesis in
FIG. 24 of
##EQU34##
Consider now an extension to the partial hypothesis in FIG. 25. If a
partial hypothesis that is to be extended contains an open morpheme, then,
in a preferred embodiment, that hypothesis can only be extended by
aligning another morpheme from the source structure with that open
morpheme. When such an extension is made, there are two possibilities: 1)
the open morpheme is kept open in the extended partial hypothesis,
indicating that more source morphemes are to be aligned with that open
target morpheme, or 2) the open morpheme is closed indicating that no more
source morphemes are to be aligned with that target morpheme. These two
cases are illustrated in FIGS. 26 and 27.
In FIG. 26, an extension is made of the partial alignment in FIG. 25 by
aligning the additional French morpheme jeune with the English morpheme
girl. In this example the English morpheme girl is then closed in the
resultant partial hypothesis. The extension score for this example is
##EQU35##
Here, the first quotient adjusts the fertility score for the partial
hypothesis by dividing out the estimate of the probability that girl is
aligned with at least two French morphemes and by multiplying in an
estimate of the probability that girl is aligned with exactly two French
morphemes. As in the other examples, the second and third factors are
estimates of the lexical and distortion probabilities associated with this
extension.
In FIG. 27, the same extension is made as in FIG. 26, except here the
English morpheme girl is kept open after the extension, hence the + sign.
The extension score for this example is
##EQU36##
Here, the factor of 3 is the adjustment to the combinatorial factor for the
partial hypothesis. Since the score for the partial hypothesis in FIG. 26
already has a combinatorial factor of 2, the score for the resultant
partial hypothesis in FIG. 27, will have a combinatorial factor of
2.times.3=3 . The quotient adjusts the fertility score for the partial
hypothesis to reflect the fact that in further extensions of this
hypothesis girl will be aligned with at least three French morphemes.
Another type of extension performed in the hypothesis search is one in
which two additional target morphemes are appended to the partial target
structure of the partial hypothesis being extended. In this type of
extension, the first of these two morphemes is assigned a fertility of
zero and the second is aligned with a single morpheme from the source
structure. This second target morpheme may be either open or closed.
FIG. 29 shows an extension of the partial hypothesis in FIG. 28 by the two
target morphemes up her, in which her is aligned with the source morpheme
sa. The score for this extension is
l(up.vertline.girl,to.sub.13 wake)l(her.vertline.to.sub.--
wake,up)n(0.vertline.up)n(1.vertline.her)t(sa.vertline.her)d(6.vertline.6)
.(188)
Here, the first two factors are the trigram language model estimates of the
probabilities with which up follows girl to.sub.-- wake, and with which
her follows to.sub.-- wake up, respectively. The third factor is the
fertility parameter that serves as an estimate of the probability that up
is aligned with no source morphemes. The fourth, fifth, and sixth factors
are the appropriate fertility, lexical, and distortion parameters
associated with the target morpheme her in this partial alignment.
FIG. 30 shows a similar extension by up her. The difference with the
extension in FIG. 29 is that in FIG. 30, the source morpheme her is open.
The score for this extension is
##EQU37##
The score for this extension differs from the score in Equation 188 in that
the fertility parameter n(1.vertline.her) is replaced by the combinatorial
factor 2 and the sum of fertility parameters which provides an estimate of
the probability that her will be aligned with at least two source
morphemes.
A remaining type of extension is where a partial hypothesis is extended by
an additional connection which aligns a source morpheme with the null
target morpheme. The score for this type of extension is similar to those
described above. No language model score is factored in, and scores from
the translation model are factored in, in accordance with the
probabilities associated with the null word as described in the section
entitled Translation Models and Parameter Estimation.
Selection of Hypotheses to Extend 5402 (Brown et al. Section 14.3)
Throughout the hypothesis search process, partial hypotheses are maintained
in a set of priority queues. In theory, there is a single priority queue
for each subset of positions in the source structure. So, for example, for
the source structure oui , oui, three positions: oui is in position 1; a
comma is in position 2; and oui is in position 3, and there are therefore
2.sup.3 subsets of positions { }, {1}, {2}, {3}, {1,2}, {1,3}, {2,3}, and
{1,2,3}. In practice, these priority queues are initialized only on
demand, and many less than the full number of queues possible are used in
the hypothesis search. In a preferred embodiment, each partial hypothesis
is comprised of a sequence of target morphemes, and these morphemes are
aligned with a subset of source morphemes. Corresponding to that subset of
source morphemes is a priority queue in which the partial hypothesis is
stored. The partial hypotheses within a queue are prioritized according to
the scores associated with those hypotheses. In certain preferred
embodiments the priority queues are limited in size and only the 1000
hypothesis with the best scores are maintained.
The set of all subsets of a set of source structure positions can be
arranged in a subset lattice. For example, the subset lattice for the set
of all sets of the set {1,2,3} is shown in FIG. 31. In a subset lattice, a
parent of a set S is any which contains one less element than S, and which
is also a subset of S. In FIG. 31 arrows have been drawn from each set in
the subset lattice to each of its parents. For example, the set {2} is a
parent of the set {1,2}.
A subset lattice defines a natural partial ordering on a set of sets. Since
the priority queues used in hypothesis search are associated with subsets,
a subset lattice also defines a natural partial ordering on the set of
priority queues. Thus in FIG. 31, there are two parents of the priority
queue associated with the subset of source structure positions {1,3}.
These two parents are the priority queues associated with the set {1} and
{3}. A priority queue Q.sub.1 is said to be an ancestor of another
priority Q.sub.2 if 1) Q.sub.1 is not equal to Q.sub.2, and 2) Q.sub.1 is
a subset of Q.sub.2. If Q.sub.1 is an ancestor of Q.sub.2, then Q.sub.2 is
said to be to be a descendant of Q.sub.1.
Considering now the process by which a set of partial hypotheses are
selected in step 5402 to be extended in step 5404, when step 5402 is
invoked, it is invoked with a list of partial hypotheses that were either
1) created by the initialization step 5401, or 2) created as the results
of extensions in step 5404 on a previous pass through the loop comprised
of steps 5402, 5403, and 5404. These partial hypotheses are stored in
priority queues according to the sets of source morphemes they account
for. For example, the partial hypothesis in FIG. 32 would be stored in the
priority queue associated with the set {2,3}, since it accounts for the
source morphemes in positions 2 and 3.
A priority queue is said to be active if there are partial hypotheses
stored in it. An active priority queue is said to be on the frontier if it
has no active descendent. The cardinality of a priority queue is equal to
the number of elements in the subset with which it is associated. So, for
example, the cardinality of the priority queue which is associated with
the set {2,3} is 2.
The process in step 5402 functions by assigning a threshold to every active
priority queue and then places on the list of partial hypotheses to be
extended every partial hypothesis on an active priority queue that has an
a score that is greater than the threshold for that priority queue. This
is depicted in FIG. 33. First, in step 6601 the threshold for every active
priority queue is initialized to infinity, in practice, some very large
number. Second, in step 6602, thresholds are determined for every priority
queue on the frontier.
The method by which these thresholds are computed is best described by
first describing what the normalizer of a priority queue is. Each priority
queue on the frontier corresponds to a set of positions of source
morphemes. At each position of these positions is a particular source
morpheme. Associated with each morpheme is a number, which in a preferred
embodiment is the unigram probability of that source morpheme. These
unigram probabilities are estimated by transducing a large body of source
text and simply counting the frequency with which the different source
morphemes occur. The normalizer for a priority queue is defined to be the
product of all the unigram probabilities for the morphemes at the
positions in the associated set of source structure positions. For
example, the normalizer for the priority queue associated with the set
{2,3} for the source structure la jeune fille V.sub.-- past.sub.-- 3s
reveiller sa mere is:
normalizer({2,3})=Pr(jeune)Pr(fille). (190)
For each priority queue Q on the frontier define the normed score of Q to
be equal to the score of the partial hypothesis with the greatest score in
Q divided by the normalizer for Q. Let Z be equal to the maximum of all
normed scores for all priority queues on the frontier. The threshold
assigned to a priority queue Q on the frontier is then equal to Z times
the normalizer for that priority queue divided by a constant which in a
preferred embodiment is 45.
After step 6602, thresholds have been assigned to the priority queues on
the frontier, a loop is performed in steps 6604 through 6610. The loop
counter i is equal to a different cardinality on each iteration of the
loop. The counter i is initialized in step 6604 to the largest cardinality
of any active priority queue, in other words, i is initialized to the
maximum cardinality of any priority queue on the frontier. On each
iteration of the loop the value of i is decreased by 1 until i is equal to
0, at which point the test 6604 is satisfied and the process of selecting
partial hypotheses to be extended is terminated.
Inside the loop through cardinalities is another loop in steps 6606 through
6609. This is a loop through all active priority queues of a given
cardinality. In this loop each priority queue of cardinality i is
processed in step 6608.
A schematic flow diagram for this processing step 6608 is shown in FIG. 34.
The priority queue Q to be processed enters this step at 6701. Steps 6704
through 6707 perform a loop through all partial hypotheses i in the
priority queue Q which are greater than the threshold associated with Q.
At step 6705 the partial hypothesis i is added to the list of partial
hypotheses to be extended. At step 6706 i is used to adjust the thresholds
of all active priority queues which are parents of Q. These thresholds are
then used when priority queues of lower priority are processed in the loop
beginning at step 6604 in FIG. 33.
Each priority queue which is a parent of partial hypothesis i at step 6706
contains partial hypotheses which account for one less source morpheme
than the partial hypothesis i does.
For example, consider the partial hypothesis depicted in FIG. 26. Suppose
this is the partial hypothesis i. The two target morphemes the and girl
are aligned with the three source morphemes la, jeune, and fille which are
in source structure positions 1, 2, and 3 respectively. This hypothesis i
is therefore in the priority queue corresponding to the set {1,2,3}. The
priority queues that are parents of this hypothesis correspond to the sets
{1,2}, {1,3}, and {2,3}. We can use partial hypothesis i to adjust the
threshold in each of these priority queues, assuming they are all active,
by computing a parent score, score.sub.p from the score score.sub.i
associated with the partial hypothesis i. A potentially different parent
score is computed for each active parent priority queue. That parent score
is then divided by a constant, which in a preferred embodiment is equal to
45. The new threshold for that queue is then set to the minimum of the
previous threshold and that parent score.
These parent scores are computed by removing from score, the contributions
for each of the source morphemes la, jeune, and fille. For example, to
adjust the threshold for the priority queue {2,3}, it is necessary to
remove the contribution to the score, associated with the source morpheme
in position 1, which is la. This morpheme is the only morpheme aligned
with the, so the language model contribution for the must be removed, as
well as the translation model contributions associated with la. Therefore,
##EQU38##
As another example, to adjust the threshold for the priority queue {1,3},
it is necessary to remove the contribution to the score, associated with
the source morpheme in position 2, which is jeune. This morpheme is one of
two aligned with the target morpheme girl. If the connection between girl
and jeune is removed from the partial alignment in FIG. 26, there is still
a connection between girl and fille. In other words, girl is still needed
in the partial hypothesis to account for fille. Therefore, no language
model component is removed. The parent score in this case is:
##EQU39##
Here, the first quotient adjust the fertility score, the second adjusts the
lexical score and the third adjusts the distortion score.
With some thought, it will be clear to one skilled in the art how to
generalize from these examples to other situations. In general, a parent
score is computed by removing a connection from the partial alignment
associated with the partial hypothesis i. Such a connection connects a
target morpheme t in the partial target structure associated with the
partial hypothesis i and a source morpheme s in a source structure. If
this connection is the only connection to the target morpheme t, then the
language model score for t is divided out, otherwise it is left in. The
lexical and distortion scores associated with the source morpheme s are
always divided out, as is the fertility score associated with the target
morpheme t. If n connections remain to the target morpheme t, since n+1
source morphemes are aligned with t in the partial hypothesis i, then the
open fertility score serving as an estimate of the probability that at
least n+1 source morphemes will be aligned with t is multiplied in.
When the list of hypotheses to be extended that is created in step 5402 is
empty the search terminates.
Refer now to step 5404 in FIG. 21. This step extends a list of partial
hypotheses. An embodiment of the method by which this extension takes
place is documented in the pseudo code in FIG. 35.
The procedure extend.sub.-- partial.sub.-- hypotheses.sub.-- on.sub.-- list
takes as input a list of hypotheses to be extended. Lines 1 through 8
contain a loop through all the partial hypotheses on the list which are
extended in turn. In line 3 the variable h is set to the partial
hypothesis being extended on iteration of the loop. For each hypothesis,
h, that is extended, it can be extended by aligning an additional source
morpheme with a morpheme in an hypothesized target structure. In lines 4
through 7 a loop is made through every position p in the source structure.
In certain embodiments, a loop may be made only through the first n source
positions that are not already aligned in the partial hypothesis h. In a
preferred embodiment n is set to 4. At line 5 a test is made to see if the
source morpheme at position p is already aligned by the partial hypothesis
with an hypothesized target morpheme. If it is not, then at line 6 a call
is made to the procedure extend.sub.-- h.sub.-- by.sub.--
accounting.sub.-- for.sub.-- source.sub.-- morpheme.sub.-- in.sub.--
position.sub.-- p, which creates those extensions of partial hypothesis h
which align the source morpheme at position p with an hypothesized target
morpheme.
Pseudo code for the procedure extend.sub.-- h.sub.-- by.sub.--
accounting.sub.-- for.sub.-- source.sub.-- morpheme.sub.-- in.sub.--
position.sub.-- p is contained in lines 10 through 31 of figure. At line
11 a check is made to determine if the partial hypothesis h is open, in
other words, if it contains an open target morpheme. If it is open then
extensions are made in lines 12 through 14. On line 12, the variable q is
set to the position in the hypothesized partial target structure of the
open morpheme. Each of these extensions made in lines 12 through 14 are
made by adding a connection to the partial alignment of h. Each such
connection is a connection from the morpheme at position p in the source
structure to the open morpheme at position q in the target structure. On
line 13, an extension is created in which a connection from p to q is
added to the partial alignment of h and in which the morpheme at position
t is kept open. On line 14, an extension is created in which a connection
from p to q is added to the partial alignment of h and in which the
morpheme at position t is closed.
Extensions of partial hypotheses h which are closed are made in lines 17
through 29. First, in line 17 the variable s is set to the identity of the
source morpheme at position p in the source structure. This morpheme will
have a number of possible target translations. In terms of the translation
model, this means that there will be a number of target morphemes t for
which the lexical parameter t(t.vertline.s) is greater than a certain
threshold, which in an embodiment is set equal to 0.001. The list of such
target morphemes for a given source morpheme s can be precomputed. In
lines 18 through 29 a loop is made through a list of the target morphemes
for the source morpheme s. The variable t is set to the target morpheme
being processed in the loop. On line 20, an extension is made in which the
target morpheme t is appended to the right end of the partial target
structure associated with h and then aligned with the source morpheme at
position p, and in which the target morpheme t is open in the resultant
partial hypothesis. On line 21, an extension is made in which the target
morpheme t is appended to the right end of the partial target structure
associated with h and then aligned with the source morpheme at position p,
and in which the target morpheme t is closed in the resultant partial
hypothesis. On line 22, an extension is made in which the target morpheme
t is appended to the null target morpheme in the partial target structure
associated with hypothesis h. It is assumed throughout this description of
hypothesis search that every partial hypothesis comprises a single null
target morpheme.
The remaining types of extensions to be performed are those in which the
target structure is extended by two morphemes. In such extensions, the
source morpheme at position p is aligned with the second of these two
target morphemes. On line 23, a procedure is called which creates a list
of target morphemes that can be inserted between the last morpheme on the
right of the hypothesis h and the hypothesized target morpheme, t. The
lists of target morphemes created by this procedure can be precomputed
from language model parameters. In particular, suppose t.sub.r is the last
morpheme on the right of the partial target structure comprised by the
partial hypothesis h. For any target morpheme t.sub.1 the language model
provides a score for the three-word sequence t.sub.r t.sub.1 t. In one
preferred embodiment this score is equal to an estimate of 1-gram
probability for the morpheme t.sub.r, multiplied by an estimate of the
probability with 2-gram conditional probability with which t.sub.1 follows
t.sub.r, multiplied by an estimate of the 3-gram conditional probability
with which t follows the pair t.sub.r t.sub.1. By computing such a score
for each target morpheme t.sub.1, the target morphemes can be ordered
according to these scores. The list returned by the procedure called on
line 23 is comprised of the m best t.sub.1 's which have scores greater
than a threshold z. In one embodiment, z is equal to 0.001 and m is equal
to 100.
The loop on lines 24 through 28 makes extensions for each t.sub.1 on the
list created on line 23. On lines 26 and 27, extensions are made in which
the pair of target morphemes t.sub.1,t is appended to the end of the
partial target structure comprised by the partial hypothesis h, and in
which the source morpheme at position p is aligned with t. The hypotheses
which result from extensions made on line 23 are open and the hypotheses
which result from extensions made on line 23 are closed.
Table 2 shows a hypothetical example of target hypotheses E.sub.1, E.sub.2,
and E.sub.3. In this example, the target words are English language words.
TABLE 2
______________________________________
Target Hypotheses E.sub.h
E.sub.h
e.sub.h,1
e.sub.h,2
e.sub.h,3
e.sub.h,4
e.sub.h,5
e.sub.h,6
______________________________________
E.sub.1
The key is in the door .
E.sub.2
The key is in the gate .
E.sub.3
The wrench is in the door .
______________________________________
Preferably, each target hypothesis comprises a series of target words
selected from a vocabulary comprising words in the second language, and a
null word representing the absence of a word. In Table 2, all of the
target hypotheses E.sub.h will be considered to include the "null" word.
Returning to FIG. 1, the translation apparatus comprises a language model
match score generator 16 for generating, for each target hypothesis, a
language model match score comprising an estimate of the probability of
occurrence of the series of words in the target hypothesis. Sections 6 and
7 of Brown et al, cited above, describe an example of a language model
match score generator, and are incorporated herein by reference and set
forth in full below. While any known language model may be used to
estimate the probability of occurrence of the series of words in the
target hypothesis, Brown et al describe an n-gram language model
comprising a 1-gram model, a 2-gram model, and a 3-gram model combined by
parameters whose values are obtained by interpolated estimation.
The translation apparatus further comprises an alignment identifier 18 for
identifying at least one alignment between the input series of source
words and each target hypothesis. The alignment connects each source word
with at least one target word in the target hypothesis.
The example of a language model match score generator (described in
sections 6 and 7 of Brown et al) is now recited herein in full.
TARGET LANGUAGE MODEL (Brown et al. Section 6)
The inventions described in this specification employ probabilistic models
of the target language in a number of places. These include the target
structure language model 705, and the class language model used by the
decoder 404. As depicted in FIG. 36, the role of a language model is to
compute an a priori probability or score of a target structure.
Language models are well known in the speech recognition art. They are
described in the article "Self-Organized Language Modeling for Speech
Recognition", by F. Jelinek, appearing in the book Readings in Speech
Recognition edited by A. Waibel and K. F. Lee and published by Morgan
Kaufmann Publishers, Inc., San Matco, Calif., in 1990. They are also
described in the article "A Tree-Based Statistical Model for Natural
Language Speech Recognition", by L. Bahl et al., appearing in the July
1989 Volume 37 of the IEEE Transactions on Acoustics, Speech and Signal
Processing. These articles are included by reference herein. They are
further described in the paper "Trainable Grammars for Speech
Recognition", by J. Baker, appearing in the 1979 Proceedings of the Spring
Conference of the Acoustical Society of America.
In some embodiments of the present inventions, the target structure
consists of a sequence of morphs. In these embodiments, n-gram language
models, as described in the aforementioned article by F. Jelinek, can be
used. In other embodiments, the target structure comprises parse trees of
the target language. In these embodiments, language models based on
stochastic context-free grammars, as described in the aforementioned
articles by F. Jelinek and the aforementioned paper by J. Baker, can be
used.
In addition, decision tree language models, as described in the
aforementioned paper by L. Bahl, et al. can be adapted by one skilled in
the art to model a wide variety of target structures.
Perplexity (Brown et al. Section 6.1)
The performance of a language model in a complete system depends on a
delicate interplay between the language model and other components of the
system. One language model may surpass another as part of a speech
recognition system but perform less well in a translation system.
Since it is expensive to evaluate a language model in the context of a
complete system, it is useful to have an intrinsic measure of the quality
of a language model. One such measure is the probability that the model
assigns to the large sample of target structures. One judges as better the
language model which yields the greater probability. When the target
structure is a sequence of words or morphs, this measure can be adjusted
so that its takes account of the length of the structures. This leads to
the notion of the perplexity of a language model with respect to a sample
of text S:
##EQU40##
where .vertline.S.vertline. is the number of morphs of S. Roughly
speaking, the perplexity is the average number of morphs which the model
cannot distinguish between, in predicting a morph of S. The language model
with the smaller perplexity will be the one which assigns the larger
probability to S.
Because perplexity depends not only on the language model but also on the
sample of text, it is important that the text be representative of that
for which the language model is intended. Because perplexity is subject to
sampling error, making fine distinctions between language models may
require that the perplexity be measured with respect to a large sample.
n-Gram Language Models (Brown et al. Section 6.2)
n-Gram language models will now be described. For these models, the target
structure consists of a sequence of morphs.
Suppose m.sub.1 m.sub.2 m.sub.3 . . . m.sub.k be a sequence of k morphs
m.sub.i. For 1.ltoreq.i.ltoreq.j.ltoreq.k, let m.sub.i.sup.j denote the
subsequence m.sub.i.sup.j .ident.m.sub.i m.sub.i+1 . . . m.sub.j. For any
sequence, the probability of a m.sub.1.sup.k .ident.is equal to the
product of the conditional probabilities of each morph m.sub.i given the
previous morphs m.sub.1.sup.i-1 :
Pr(m.sub.1.sup.k)=Pr(m.sub.1)Pr(m.sub.2 .vertline.m.sub.1)Pr(m.sub.3
.vertline.m.sub.1 m.sub.2) . . . Pr(m.sub.i .vertline.m.sub.1.sup.i-1) . .
. Pr(m.sub.k .vertline.m.sub.1.sup.k-1). (204)
The sequence m.sub.1.sup.i-1 is called the history of the morph m.sub.i in
the sequence.
For an n-gram model, the conditional probability of a morph in a sequence
is assumed to depend on its history only through the previous n-1 morphs:
Pr(m.sub.i .vertline.m.sub.1.sup.i-1).apprxeq.Pr(m.sub.i
.vertline.m.sub.i-n+1.sup.i-1) (205)
For a vocabulary of size V, a 1-gram model is determined by V-1 independent
numbers, one probability Pr(m) for each morph m in the vocabulary, minus
one for the constraint that all of the probabilities add up to 1. A 2-gram
model is determined by V.sup.2 -1 independent numbers, V(V-1) conditional
probabilities of the form Pr(m.sub.2 .vertline.m.sub.1) and V-1 of the
form Pr(m). In general, an n-gram model is determined by V.sup.n -1
independent numbers, V.sup.n-1 (V-1) conditional probabilities of the form
Pr(m.sub.n .vertline.m.sub.1.sup.n-1), called the order-n conditional
probabilities, plus V.sup.n-1 -1 numbers which determine an (n-1)-gram
model.
The order-n conditional probabilities of an n-gram model form the
transition matrix of an associated Markov model. The states of this Markov
model are sequences of n-1 morphs, and the probability of a transition
from the state m.sub.1 m.sub.2 . . . m.sub.n-1 to the state m.sub.2
m.sub.3 . . . m.sub.n is Pr(m.sub.n .vertline.m.sub.1 m.sub.2 . . .
m.sub.n-1). An n-gram language model is called consistent if, for each
string m.sub.1.sup.n-1, the probability that the model assigns to
m.sub.1.sup.n-1 is the steady state probability for the state
m.sub.1.sup.n-1 of the associated Markov model.
Simple n-Gram Models (Brown et al. Section 6.3)
The simplest form of an n-gram model is obtained by assuming that all the
independent conditional probabilities are independent parameters. For such
a model, values for the parameters can be determined from a large sample
of training text by sequential maximum likelihood training. The order
n-probabilities are given by
##EQU41##
where f(m.sub.1.sup.i) is the number of times the string of morphs
m.sub.1.sup.i appears in the training text. The remaining parameters are
determined inductively by an analogous formula applied to the
corresponding n-1-gram model. Sequential maximum likelihood training does
not produce a consistent model, although for a large amount of training
text, it produces a model that is very nearly consistent.
Unfortunately, many of the parameters of a simple n-gram model will not be
reliably estimated by this method. The problem is illustrated in Table 16,
which shows the number of 1-, 2-, and 3-grams appearing with various
frequencies in a sample of 365,893,263 words of English text from a
variety of sources. The vocabulary consists of the 260,740 different words
plus a special unknown word into which all other words are mapped. Of the
6.799.times.10.sup.10 2-grams that might have occurred in the data, only
14,494,217 actually did occur and of these, 8,045,024 occurred only once
each. Similarly, of the 1.773.times.10.sup.16 3-grams that might have
occurred, only 75,349,888 actually did occur and of these, 53,737,350
occurred only once each. These data and Turing's formula imply that 14.7
percent of the 3-grams and for 2.2 percent of the 2-grams in a new sample
of English text will not appear in the original sample. Thus, although any
3-gram that does not appear in the original sample is rare, there are so
many of them that their aggregate probability is substantial.
TABLE 16
______________________________________
Number of n-grams with various frequencies in
365,893,263 words of running text.
Count 1-grams 2-grams 3-grams
______________________________________
1 36,789 8,045,024 53,737,350
2 20,269 2,065,469 9,229,958
3 13,123 970,434 3,653,791
>3 135,335 3,413,290 8,728,789
>0 205,516 14,494,217 75,349,888
.gtoreq.0
260,741 6.799 .times. 10.sup.10
1.773 .times. 10.sup.16
______________________________________
Thus, as n increases, the accuracy of a simple n-gram model increases, but
the reliability of the estimates for its parameters decreases.
Smoothing (Brown et al. Section 6.4)
A solution to this difficulty is provided by interpolated estimation, which
is described in detail in the paper "Interpolated estimation of Markov
source parameters from sparse data", by F. Jelinek and R. Mercer and
appearing in Proceeding of the Workshop on Pattern Recognition in
Practice, published by North-Holland, Amsterdam, The Netherlands, in May
1980. Interpolated estimation combines several models into a smoothed
model which uses the probabilities of the more accurate models where they
are reliable and, where they are unreliable, falls back on the more
reliable probabilities of less accurate models. If Pr.sup.(j) (m.sub.i
.vertline.m.sub.1.sup.i-1) is the jth language model, the smoothed model,
Pr(m.sub.i .vertline.m.sub.1.sup.i-1), is given by
##EQU42##
The values of the .lambda..sub.j (m.sub.1.sup.i-1) are determined using the
EM method, so as to maximize the probability of some additional sample of
training text called held-out data. When interpolated estimation is used
to combine simple 1-, 2-, and 3-gram models, the .lambda.'s can be chosen
to depend on m.sub.1.sup.i-1 only through the count of m.sub.i-2
m.sub.i-1. Where this count is high, the simple 3-gram model will be
reliable, and, where this count is low, the simple 3-gram model will be
unreliable.
The inventors constructed an interpolated 3-gram model in which the
.lambda.'s were divided into 1782 different sets according to the 2-gram
counts, and determined from a held-out sample of 4,630,934 million words.
The power of the model was tested using the 1,014,312 word Brown corpus.
This well known corpus, which contains a wide variety of English text, is
described in the book Computational Analysis of Present-Day American
English, by H. Kucera and W. Francis, published by Brown University Press,
Providence, R.I., 1967. The Brown corpus was not included in either the
training or held-out data used to construct the model. The perplexity of
the interpolated model with respect to the Brown corpus was 244.
n-Gram Class Models (Brown et al. Section 6.5)
Clearly, some words are similar to other words in their meaning and
syntactic function. For example, the probability distribution of words in
the vicinity of Thursday is very much like that for words in the vicinity
of Friday. Of course, they will not be identical: people rarely say Thank
God it's Thursday or worry about Thursday the 13.sup.th.
In class language models, morphs are grouped into classes, and morphs in
the same class are viewed as similar. Suppose that c is a map that
partitions the vocabulary of V morphs into C classes by assigning each
morph m to a class c(m). An n-gram class model based on c is an n-gram
language model for which
Pr(m.sub.k .vertline.m.sub.1.sup.k-1)=Pr(m.sub.k
.vertline.c.sub.k)Pr(c.sub.k .vertline.c.sub.1.sup.k-1) (208)
where c.sub.i =c(m.sub.i). An n-gram class model is determined by C.sup.n
-1+V-C independent numbers, V-C of the form Pr(m.sub.i .vertline.c.sub.i),
plus C.sup.n -1 independent numbers which determine an n-gram language
model for a vocabulary of size C. If C is much smaller than V, these are
many fewer numbers than are required to specify a general n-gram language
model.
In a simple n-gram class model, the C.sup.n -1+V-C independent
probabilities are treated as independent parameters. For such a model,
values for the parameters can be determined by sequential maximum
likelihood training. The order n probabilities are given by
##EQU43##
where f(c.sub.1.sup.i) is the number of times that the sequence of classes
c.sub.1.sup.i appears in the training text. (More precisely,
f(e.sub.1.sup.i) is the number of distinct occurrences in the training
text of a consecutive sequence of morphs m.sub.1.sup.i for which c.sub.k
=c(m.sub.k) for 1.ltoreq.k.ltoreq.i.)
Classes (Brown et al. Section 7)
The invention described in this specification employ classes of morphs or
words in a number of places. These include the class language model used
by the decoder 702 and described in Section 14, and some embodiments of
the target structure language model 705.
The inventors have devised a number of methods for automatically
partitioning a vocabulary into classes based upon frequency or coocurrence
statistics or other information extracted from textual corpora or other
sources. In this section, some of these methods will be explained. An
application to construction of syntactic classes of words will be
described. A person skilled in the art can easily adapt the methods to
other situations. For example, the methods can be used to construct
classes of morphs instead of classes of words. Similarly, they can be used
to construct classes based upon cooccurrence statistics or statistics of
word alignments in bilingual corpora.
Maximum Mutual Information Clustering (Brown et al. Section 7.1)
A general scheme for clustering a vocabulary into classes is depicted
schematically in FIG. 37. It takes as input a desired number of classes C
3101, a vocabulary 3102 of size V, and a model 3103 for a probability
distribution P(w.sub.1,w.sub.2) over bigrams from the vocabulary. It
produces as output a partition 3104 of the vocabulary into C classes. In
one application, the model 3103 can be a 2-gram language model as
described in Section 6, in which case P(w.sub.1,w.sub.2) would be
proportional to the number of times that the bigram w.sub.1 w.sub.2
appears in a large corpus of training text.
Let the score .psi.(C) of a partition C be the average mutual information
between the classes of C with respect to the probability distribution
P(w.sub.1,w.sub.2):
##EQU44##
In this sum, c.sub.1 and c.sub.2 each run over the classes of the partition
C, and
##EQU45##
The scheme of FIG. 37 chooses a partition C for which the score average
mutual information .psi.(C) is large.
A Clustering Method (Brown et al. Section 7.2)
One method 3204 for carrying out this scheme is depicted in FIG. 38. The
method proceeds iteratively. It begins (step 3203) with a partition of
size V in which each word is assigned to a distinct class. At each stage
of the iteration (Steps 3201 and 3202), the current partition is replaced
by a new partition which is obtained by merging a pair of classes into a
single class. The pair of classes to be merged is chosen so that the score
of the new partition is as large as possible. The method terminates after
V-C iterations, at which point the current partition contains C classes.
In order that it be practical, the method 3204 must be implemented
carefully. At the i.sup.th iteration, a pair of classes to be merged must
be selected from amongst approximately (V-i).sup.2 /2 pairs. The score of
the partition obtained by merging any particular pair is the sum of
(V-i).sup.2 terms, each of which involves a logarithm. Since altogether
there are V-C merges, this straight-forward approach to the computation is
of order V.sup.5. This is infeasible, except for very small values of V. A
more frugal organization of the computation must take advantage of the
redundancy in this straight-forward calculation.
An implementation will now be described in which the method 3204 executes
in time of order V.sup.3. In this implementation, the change in score due
to a merge is computed in constant time, independent of V.
Let C.sub.k denote the partition after V-k merges. Let C.sub.k (1), C.sub.k
(2), . . . , C.sub.k (k) denote the k classes of C.sub.k. Let p.sub.k
(l,m)=P(C.sub.k (l), C.sub.k (m)) and let
##EQU46##
Let I.sub.k =.psi.(C.sub.k) be the score of C.sub.k, so that
##EQU47##
Let I.sub.k (i,j) be the score of the partition obtained from C.sub.k by
merging classes C.sub.k (i) and C.sub.k (j), and let L.sub.k (i,j)=I.sub.k
-I.sub.k (i,j) be the change in score as a result of this merge. Then
##EQU48##
In these and subsequent formulae, i.orgate.j denotes the result of the
merge, so that, for example
##EQU49##
The key to the implementation is to store and inductively update the
quantities
##EQU50##
Note that if I.sub.k, s.sub.k (i), and s.sub.k (j), are known, then the
majority of the time involved in computing I.sub.k (i,j) is devoted to
computing the sums on the second line of equation 18. Each of these sums
has approximately V-k terms and so this reduces the problem of evaluating
I.sub.k (i,j) from one of order V.sup.2 to one of order V.
Suppose that the quantities shown in Equation 222 are known at the
beginning of an iteration. Then the new partition C.sub.k-1 is obtained by
merging the pair of classes C.sub.k (i) and C.sub.k (j), i<j, for which
L.sub.k (i,j) is smallest. The k-1 classes of the new partition are
C.sub.k-1 (1), C.sub.k-1 (2), . . . , C.sub.k-1 (k-1) with
C.sub.k-1 (l)=C.sub.k (l) if l.noteq.i,j
C.sub.k-1 (i)=C.sub.k (i).orgate.C.sub.k (j)
C.sub.k-1 (j)=C.sub.k (k) if j<k
Obviously, I.sub.k-1 =I.sub.k (i,j). The values of p.sub.k-1, pl.sub.k-1,
pr.sub.k-1, and q.sub.k-1 can be obtained easily from p.sub.k, pl.sub.k,
pr.sub.k, and q.sub.k. If l and m denote indices neither of which is equal
to either i or j, then
s.sub.k-1 (l)=s.sub.k (l)-q.sub.k (l,i)-q.sub.k (i,l)-q.sub.k (l,j)-q.sub.k
(j,l)+q.sub.k-1 (l,i)+q.sub.k-1 (i,l)
s.sub.k-1 (j)=s.sub.k (k)-q.sub.k (k,i)-q.sub.k (i,k)-q.sub.k (k,j)-q.sub.k
(j,k)+q.sub.k-1 (j,i)+q.sub.k-1 (i,j)
L.sub.k-1 (l,m)=L.sub.k (l,m)-q.sub.k (.orgate.m,i)-q.sub.k
(i,l.orgate.m)-q.sub.k (l.orgate.m,j)-q.sub.k (j,l.orgate.m)+q.sub.k-1
(l.orgate.m,i)+q.sub.k-1 (i,l.orgate.m)
L.sub.k-1 (l,j)=L.sub.k (l,k)-q.sub.k (.orgate.k,i)-q.sub.k
(i,l.orgate.k)-q.sub.k (l.orgate.k,j)-q.sub.k (j,l.orgate.k)+q.sub.k-1
(l.orgate.j,i)+q.sub.k-1 (i,l.orgate.j)
L.sub.k-1 (j,l)=L.sub.k-1 (l,j) (223)
Finally, s.sub.k-1 (i) and L.sub.k-1 (l,i) are determined from equations
218 and 219.
This update process requires order V.sup.2 computations. Thus, by this
implementation, each iteration of the method requires order V.sup.2 time,
and the complete method requires order V.sup.3 time.
The implementation can improved further by keeping track of those pairs l,m
for which p.sub.k (l,m) is different from zero. For example, suppose that
P is given by a simple bigram model trained on the data described in Table
16 of Section 6. In this case, of the 6.799.times.10.sup.10 possible word
2-grams w.sub.1,w.sub.2, only 14,494,217 have non-zero probability. Thus,
in this case, the sums required in equation 218 have, on average, only
about 56 non-zero terms instead of 260,741 as might be expected from the
size of the vocabulary.
An Alternate Clustering Method (Brown et al. Section 7.3)
For very large vocabularies, the method 3204 may be too computationally
costly. The following alternate method can be used. First, the the words
of the vocabulary are arranged in order of frequency with the most
frequent words first. Each of the first C words is assigned to its own
distinct class. The method then proceeds iteratively through V-C steps. At
the k.sup.th step the (C+k).sup.st most probable word is assigned to a new
class. Then, two of the resulting C+1 classes are merged into a single
class. The pair of classes that is merged is the one for which the loss in
average mutual information is least. After V-C steps, each of the words in
the vocabulary will have been assigned to one of C classes.
Improving Classes (Brown et al. Section 7.4)
The classes constructed by the clustering method 3204 or the alternate
clustering method described above can often be improved. One method of
doing this is to repeatedly cycle through the vocabulary, moving each word
to the class for which the resulting partition has the highest average
mutual information score. Eventually, no word will move and the method
finishes. It may be possible to further improve the classes by
simultaneously moving two or more words, but for large vocabularies, such
a search is too costly to be feasible.
Examples (Brown et al. Section 7.5)
The methods described above were used divide the 260,741-word vocabulary of
Table 16, Section 6, into 1000 classes. Table 17 shows some of the classes
that are particularly interesting, and Table 18 shows classes that were
selected at random. Each of the lines in the tables contains members of a
different class. The average class has 260 words. The table shows only
those words that occur at least ten times, and only the ten most frequency
words of any class. (The other two months would appear with the class of
months if this limit had been extended to twelve). The degree to which the
classes capture both syntactic and semantic aspects of English is quite
surprising given that they were constructed from nothing more than counts
of bigrams. The class {that tha theat} is interesting because although tha
and theat are English words, the method has discovered that in the
training data each of them is most often a mistyped that.
A Method for Constructing Similarity Trees (Brown et al. Section 7.6)
The clustering method 3204 can also be used to construct a similarity tree
over the vocabulary. Suppose the merging steps 3201 and 3202 of method
3204 are iterated V-1 times, resulting in a single class consisting of the
entire vocabulary. The order in which the classes are merged determines a
binary tree, the root of which corresponds to this single class and the
leaves of which correspond to the words in the vocabulary. Intermediate
nodes of the tree correspond to groupings of words intermediate between
single words and the entire vocabulary. Words that are statistically
similar with respect to the model P(w.sub.1,w.sub.2) will be close
together in the tree.
FIG. 39 shows some of the substructures in a tree constructed in this
manner using a simple 2-gram model for the 1000 most frequent words in a
collection of office correspondence.
TABLE 17
__________________________________________________________________________
Classes from a 260,741-word vocabulary
__________________________________________________________________________
Friday Monday Thursday Wednesday Tuesday Saturday Sunday weekends Sundays
Satur-
days
June March July April January December October November September August
people guys folks fellows CEOs chaps doubters commies unfortunates
blokes
down backwards ashore sideways southward northward overboard aloft
downwards adrift
water gas coal liquid acid sand carbon steam shale iron
great big vast sudden mere sheer gigantic lifelong scant colossal
man woman boy girl lawyer doctor guy farmer teacher citizen
American Indian European Japanese German African Catholic Israeli Italian
Arab
pressure temperature permeability density porosity stress velocity
viscosity gravity tension
mother wife father son husband brother daughter sister boss uncle
machine device controller processor CPU printer spindle subsystem
compiler plotter
John George James Bob Robert Paul William Jim David Mike
anyone someone anybody somebody
feet miles pounds degrees inches barrels tons acres meters bytes
director chief professor commissioner commander treasurer founder
superintendent dean cus-
todian
liberal conservative parliamentary royal progressive Tory provisional
separatist federalist PQ
had hadn't hath would've could've should've must've might've
asking telling wondering instructing informing kidding reminding
bothering thanking depos-
ing
that tha theat
head body hands eyes voice arm seat eye hair mouth
__________________________________________________________________________
TABLE 18
__________________________________________________________________________
Randomly selected word classes
__________________________________________________________________________
little prima moment's trifle tad Litle minute's tinker's hornet's
teammate's
ask remind instruct urge interrupt invite congratulate commend warn
applaud
object apologize apologise avow whish
cost expense risk profitability deferral earmarks capstone cardinality
mintage reseller
B dept. AA Whitey CL pi Namerow PA Mgr. LaRose
#Rel rel. #S Shree
S Gens nai Matsuzawa ow Kageyama Nishida Sumit Zollner Mallik
research training education science advertising arts medicine machinery
Art AIDS
rise focus depend rely concentrate dwell capitalize embark intrude
typewriting
Minister mover Sydneys Minster Miniter
running moving playing setting holding carrying passing cutting driving
fighting
court judge jury slam Edelstein magistrate marshal Abella Scalia larceny
annual regular monthly daily weekly quarterly periodic Good yearly
convertible
aware unaware unsure cognizant apprised mindful partakers
force ethic stoppage force's conditioner stoppages conditioners waybill
forwarder Atonabee
systems magnetics loggers products' coupler Econ databanks Centre
inscriber correctors
industry producers makers fishery Arabia growers addiction medalist
inhalation addict
brought moved opened picked caught tied gathered cleared hung
__________________________________________________________________________
lifted
FIGS. 2, 3, and 4 schematically show examples of possible alignments
between the hypothetical input series of source words in Table 1 and the
hypothetical input series of target words in target hypothesis E.sub.1 in
Table 2. In each alignment, each source word in the input series of source
words F is connected with at least one target word in the target
hypothesis E.sub.1 by solid line. In the alignment of FIG. 4, the second
occurrence of the source word "La" has no solid line therefrom, and is
therefore considered to be connected to the "null" word.
Tables 3, 4, and 5 provide alternate descriptions of the alignments of
FIGS. 2, 3, and 4, respectively.
TABLE 3
______________________________________
Alignment A.sub.h,1 For Hypothesis E.sub.h
(h = 1) and Source Words F
j f.sub.j
a.sub.j e.sub.aj
______________________________________
1 La 1 The
2 clef 2 key
3 est 3 is
4 dans 4 in
5 la 5 the
6 porte 6 door
______________________________________
TABLE 4
______________________________________
Alignment A.sub.h,2 For Hypothesis E.sub.h
(h = 1) and Source Words F
j f.sub.j
a.sub.j e.sub.aj
______________________________________
1 La 5 the
2 clef 2 key
3 est 3 is
4 dans 4 in
5 la 1 The
6 porte 6 door
______________________________________
TABLE 5
______________________________________
Alignment A.sub.h,2 For Hypothesis E.sub.h
(h = 1) and Source Words F
j f.sub.j
a.sub.j e.sub.aj
______________________________________
1 La 1 The
2 clef 2 key
3 est 3 is
4 dans 4 in
5 la 0 <null>
6 porte 6 door
______________________________________
In each Table, for each parameter j ranging from 1 to m (where m is the
number of words in the input series of source words), there is another
parameter a.sub.j having a single value in the range from 0 to l (wherein
l is the number of words in the target hypothesis).
For a given alignment, each word f.sub.j in the input series of source
words is connected to the word e.sub.a.sbsb.j in the target hypothesis.
In general, there are 2.sup.lm possible alignments between a series of m
source words and a series of l non-null target words, where each source
word may be connected to either the null source word or one or more
non-null target words. If each source word is constrained to be connected
to only one null or non-null target word, then there are m.sup.(l+1)
possible alignments.
Preferably, only one alignment between the input series of source words and
each target hypothesis is identified for obtaining a word match score,
described below, for each source word. The one identified alignment for
the series of source words and each target hypothesis is preferably that
which is produced by the target hypothesis generator, as described in
Section 14 of Brown et al, cited above, which is incorporated herein by
reference set forth in full above.
If the source text input device 10 comprises means for transforming the
input series of source words into a series of transformed source words,
then the alignment identifier 18 identifies at least one alignment between
the series of transformed source words and each target hypothesis. The
alignment connects each transformed source word with at least one target
word in the target hypothesis.
Returning to FIG. 1, the translation apparatus further comprises a word
match score generator 20. The word match score generator 20 generates, for
each source word and each target hypothesis, a word match score comprising
an estimate of the conditional probability P(f.vertline.e,X) of occurrence
of the source word f, given the target word e in the target hypothesis
which is connected to the source word f and given the context X of the
target word e in the target hypothesis which is connected to the source
word f.
Table 6 illustrates a hypothetical example of the context X of each target
word e.sub.a.sbsb.j in the target hypothesis E.sub.1 of Table 2 for the
alignment A.sub.1,1 of Table 3 with the input series of source words F of
Table 1.
TABLE 6
__________________________________________________________________________
Context X of Target Word e.sub.aj For Alignment A.sub.l,1
X = {e.sub.(aj-3), e.sub.(aj-2), e.sub.(aj-1), e.sub.(aj+1),
e.sub.(aj+2), e.sub.(aj+3) }
j f.sub.j
e.sub.aj
x.sub.1
x.sub.2
x.sub.3
x.sub.4
x.sub.5
x.sub.6
__________________________________________________________________________
1 La The
<null>
<null>
<null>
key
is in
2 clef
key
<null>
<null>
The is in the
3 est
is <null>
The key in the door
4 dans
in The key is the
door .
5 la the
key is in door
. <null>
6 porte
door
is in the . <null>
<null>
__________________________________________________________________________
As shown in Table 6, in this hypothetical example the context X of a
selected target word consists of the three target words preceding the
selected target word and the three target words following the selected
target word in the target hypothesis. The context also includes
punctuation and absence of words.
In general, the context of the target word e.sub.a.sbsb.j in the target
hypothesis E which is connected to a source word f.sub.j may be contained
in at least one of two or more context classes. The estimated conditional
probability of occurrence of a source word, given the target word in the
target hypothesis which is connected to the source word and given the
context of the target word connected to the source word, may comprise at
least one function having a value dependent on the class containing the
context of the target word which is connected to the source word.
Alternatively, the context may comprise at least one word having a part of
speech in the target hypothesis. The estimated conditional probability of
occurrence of a source word, given the target word in the target
hypothesis which is connected to the source word and given the context of
the target word which is connected to the source word, may comprise at
least one function having a value dependent on the part of speech in the
target hypothesis of at least one word in the context of the target word
which is connected to the source word.
In another example, the context of the target word in the target hypothesis
which is connected to a source word comprises at least one word having an
identity. The estimated conditional probability of occurrence of the
source word, given the target word in the target hypothesis which is
connected to the source word and given the context of the target word
which is connected to the source word, comprises at least one function
having a value dependent on the identity of at least one word in the
context of the target word which is connected to the source word.
Equations 3, 4, 5, and 6 are hypothetical examples of functions having
values dependent on the context of the target word which is connected to a
source word.
##EQU51##
In Equation 3, the function g.sub.1 has a value of 1, if the source word f
is "clef", if the target word e is "key", and if the word in the context X
immediately preceding "key" is "the". If these conditions are not met, the
context function g.sub.1 has a value of 0.
The hypothetical context function g.sub.2 of Equation 4 has a value of 1,
if the source word f is "clef", if the target word e is "key", and if the
word in the context X immediately preceding "key" is "car". If these
conditions are not met, the function g.sub.2 has a value of 0.
In Equation 5, the context function g.sub.3 has a value of 1 if the source
word f is "ton", if the target word e is "key", and if the word in the
context X immediately preceding "key" is "the". If these conditions are
not met, the context function g.sub.3 has a value of 0.
Finally, the hypothetical context function g.sub.4 in Equation 6 has a
value of 1 if the source word f is "ton", the target word e is "key", and
if the word in the context X immediately following "key", or the word in
the context X next following "key" is an element of the set {A, B, C, D,
E, F, G}. If these conditions are not met, the context function g.sub.4
has a value of 0.
Table 7 illustrates the evaluation of context functions g(f,e,X) for the
source word f="clef", for the target word e="key", and for the context X
of the target word "key" in the target hypothesis E.sub.1 of Table 2.
TABLE 7
__________________________________________________________________________
Context Functions g(f,e,X)
X = {e.sub.(aj-3), e.sub.(aj-2), e.sub.(aj-1),
e.sub.(aj+1), e.sub.(aj+2), e.sub.(aj+3) }
g(f.sub.j, e.sub.aj,X)
j f.sub.j
e.sub.aj
x.sub.1
x.sub.2
x.sub.3
x.sub.4
x.sub.5
x.sub.6
g.sub.1
g.sub.2
g.sub.3
g.sub.4
__________________________________________________________________________
2 clef
key
<null>
<null>
The
is in
the
1 0 0 0
__________________________________________________________________________
As shown in Table 7, the context function g.sub.1 has a value of 1, and the
context functions g.sub.2, g.sub.3 and g.sub.4 have values of 0.
As discussed above, the word match score for each source word and each
target hypothesis comprises an estimate of the conditional probability
P(f.vertline.e,X) of occurrence of the source word f given the target word
e in the target hypothesis which is connected to the source word f and
given the context X of the target word e in the target hypothesis which is
connected to the source word f. The word match score may be obtained, for
example, using a model defined by Equation 7 as follows.
##EQU52##
In Equation 7, the functions g.sub.i (f,e,X) are functions having values
dependent on the context X of the target word e which is connected to the
source word f in an alignment between an input series of source words and
a target hypothesis. The parameters .lambda..sub.(e,i) are parameters
representing the relative strength of each context function g.sub.i in
predicting the source word f from the target word e in the context X. The
quantity N(e,X) is a normalization factor which depends on the target word
e and the context X of the target word e, as shown in Equation 8.
##EQU53##
For the target word e="key" and for the hypothetical context functions
g.sub.1 through g.sub.4 of Equations 3-6, above, Equation 9 is a
hypothetical model for generating word match scores for source words f.
##EQU54##
For the purpose of Equation 9, the normalization N(e,X) is given by
Equation 10.
##EQU55##
To illustrate the use of this model, hypothetical values for the model
parameters are .lambda..sub.(e=key,1) =0.12, .lambda..sub.(e=key,2) =0.34,
.lambda..sub.(e=key,3) =0.09, and .lambda..sub.e=key,4) =0.40.
Table 8 illustrates the computation of the word match score from Equations
7 and 9 for the source word "clef", the target word "key", and the context
X of "key" in the target hypothesis E.sub.1 of Table 2.
TABLE 8
__________________________________________________________________________
Computation of Word Match Score P("clef".vertline."key", X)
X = {e.sub.(aj-3), e.sub.(aj-2), e.sub.(aj-1),
e.sub.(aj+1), e.sub.(aj+2), e.sub.(aj+3) }
g(fj, e.sub.aj, X)
f.sub.j
e.sub.aj
x.sub.1
x.sub.2
x.sub.3
x.sub.4
x.sub.5
x.sub.6
g.sub.1
g.sub.2
g.sub.3
g.sub.4
__________________________________________________________________________
clef
key
<null>
<null>
The
is
in
the
1 0 0 0
ton
key
<null>
<null>
The
is
in
the
0 0 1 0
__________________________________________________________________________
.lambda..sub.(e=key,1) = .12
.lambda..sub.(e=key,2) = .34
.lambda..sub.(e=key,3) = .09
.lambda..sub.(e=key,4) = .40
##STR1##
##STR2##
##STR3##
P("clef".vertline."key", X) = 0.507
In this hypothetical example, the conditional probability
P("clef".vertline."key", X) of occurrence of the source word "clef" given
the target word "key" in the target hypothesis which is connected to the
source word "clef" and given the context X of the target word "key" in the
target hypothesis which is connected to the source word "clef" is equal to
0.507 (from Equations 9 and 10).
If the alignment identifier 18 identifies two or more alignments between
the input series of source words and each target hypothesis, the word
match score generator generates a word match score for each source word
and each alignment and each target hypothesis. Each word match score may
be estimated using the model of Equation 7.
If the source text input device 10 comprises means for transforming the
input series of source words into a series of transformed source words,
then the word match score generator generates a word match score for each
transformed source word and each target hypothesis. The word match score
comprises an estimate of conditional probability of occurrence of the
transformed source word, given the target word in the target hypothesis
which is connected to the transformed source word and given the context of
the target word in the target hypothesis which is connected to the
transformed source word. The word match score for a transformed word may
also be estimated using the model of Equation 7.
The translation apparatus according to the invention further comprises a
translation match score generator 22. The translation match score
generator 22 generates, for each target hypothesis, a translation match
score comprising a combination of the word match scores for the target
hypothesis and the source words in the input series of source words. The
translation match score for a target hypothesis may, for example, comprise
the product of the word match scores for the target hypothesis and the
source words in the input series of source words.
Table 9 illustrates the computation of a translation match score for the
input series of source words f.sub.j of Table 1, and for the target words
e.sub.a.sbsb.j in the target hypothesis E.sub.1 of Table 2.
TABLE 9
______________________________________
Translation Match Score: Alignment = A.sub.1,1
j f.sub.j
a.sub.j e.sub.aj
P(f.sub.j .vertline.e.sub.aj,
______________________________________
X)
1 La 1 The 0.91
2 clef 2 key 0.507
3 est 3 is 0.87
4 dans 4 in 0.63
5 la 5 the 0.91
6 porte 6 door 0.79
______________________________________
##STR4##
(to illustrate the invention, this example assumes the fertility and
distortion scores, and the combinatorial factor are all equal to 1)
Each word match score P(f.sub.j .vertline.e.sub.a.sbsb.j, X) is obtained
from Equation 7. The numbers in Table 9 are hypothetical numbers. For the
purpose of illustrating the present invention, the fertility and
distortion scores of Brown et al, described above, are assumed equal to
one.
Returning to FIG. 1, the translation apparatus comprises a hypothesis match
score generator 24 for generating a target hypothesis match score for each
target hypothesis. Each target hypothesis match score comprises a
combination (for example, the product) of the language model match score
for the target hypothesis and the translation match score for the target
hypothesis. As discussed above, the language model match score may be
obtained from known language models such as the n-gram language models
described by Brown et al.
If the alignment identifier 18 identifies two or more alignments between
the input series of source words and each target hypothesis, the
translation match score for each target hypothesis comprises a combination
of the word match scores for the target hypothesis and the alignments and
the source words in the input series of source words.
If the source text input device 10 comprises means for transforming the
input series of source words into a series of transformed source words,
the translation match score comprises a combination of the word match
scores for the target hypothesis and the transformed source words.
The translation apparatus according to the invention further comprises an
output 26. The output 26 outputs the target hypothesis having the best
target hypothesis match score. The output 26 may comprise, for example, a
display or a printer.
If the source text input device 10 comprises means for transforming the
input series of source words into a series of transformed source words,
then the output 26 comprises means for synthesizing a series of output
words from the target hypothesis having the best target hypothesis match
score. The means for synthesizing output words from the target hypothesis
may comprise target transducers such as those described at Section 5 of
Brown et al (cited above) which is incorporated herein by reference. For
example, the target word "be" with a tag representing the tense of the
original input source word, may be transformed to one of the synthesized
output words {be, was, were, been, am, are, is, being} which are forms of
the verb infinitive "be".
As described above, Equation 7 is an example of a model which may be used
to obtain word match scores according to the present invention. The
context functions g.sub.i (f,e,X) of the word match score model, and the
parameters .lambda..sub.(e,i), of the word match score model may be
obtained as follows.
Candidate context functions g.sub.i (f,e,X) may be obtained, for example,
by limiting the context X of the target word in the target hypothesis to
three words to the left of the target word e and three words to the right
of the target word e, as shown in the example of Table 6, above.
Next, a training text of source language sentences and target language
sentences which are translations of each other is obtained. Corresponding
source language sentences and target language sentences which are
translations of each other may be identified, for example, by a skilled
translator. Preferably, corresponding source language sentences and target
language sentences which are translations of each other may be identified
automatically, for example, by the method described in Sections 12 and 13
of Brown et al (cited above), which are incorporated herein by reference,
and set forth in its entirety herein at this time.
ALIGNING SENTENCES (Brown et al. Section 12)
In this section, a method is described for aligning sentences in parallel
corpora, and extracting from parallel corpora pairs of sentences which are
translations of one another. These tasks are not trivial because at times
a single sentence in one corpora is translated as two or more sentences in
the other corpora. At other times a sentence, or even a whole passage, may
be missing from one or the other of the corpora.
A number of researchers have developed methods that align sentences
according to the words that they contain. (See for example, Deriving
translation data from bilingual text by R. Catizone, G. Russel, and S.
Warwick, appearing in Proceedings of the First International Acquisition
Workshop, Detroit, Mich., 1989; and "Making Connections", by M. Kay,
appearing in ACH/ALLC '91, Tempe, Ariz, 1991.) Unfortunately, these
methods are necessarily slow and, despite the potential for high accuracy,
may be unsuitable for very large collections of text.
In contrast, the method described here makes no use of lexical details of
the corpora. Rather, the only information that it uses, besides optional
information concerning anchor points, is the lengths of the sentences of
the corpora. As a result, the method is very fast and therefore practical
for application to very large collections of text.
The method was used to align several million sentences from parallel French
and English corpora derived from the proceedings of the Canadian
Parliament. The accuracy of these alignments was in excess of 99% for a
randomly selected set of 1000 alignments that were checked by hand. The
correlation between the lengths of aligned sentences indicates that the
method would achieve an accuracy of between 96% and 97% even without the
benefit of anchor points. This suggests that the method is applicable to a
very wide variety of parallel corpora for which anchor points are not
available.
Overview (Brown et al. Section 12.1)
One embodiment of the method is illustrated schematically in FIG. 40. It
comprises the steps of:
4601 and 4602. Tokenizing the text of each corpus.
4603 and 4604. Determining sentence boundaries in each corpus.
4605. Determing alignments between the sentences of the two corpora.
The basic step 4605 of determining sentence alignments is elaborated
further in FIG. 41. It comprises the steps of:
4701. Finding major and minor anchor points in each corpus. This divides
each corpus into sections between major anchor points, and subsections
between minor anchor points.
4702. Determining alignments between major anchor points.
4703. Retaining only those aligned sections for which the the number of
subsections is the same in both corpora.
4704. Determining alignments between sentences within each of the remaining
aligned subsections.
One embodiment of the method will now be explained. The various steps will
be illustrated using as an example the aforementioned parallel French and
English corpora derived from the Canadian Parliamentary proceedings. These
proceedings, called Hansards, are transcribed in both English and French.
The corpora of the example consists of the English and French Hansard
transcripts for the years 1973 through 1986.
It is understood that the method and techniques illustrated in this
embodiment and the Hansard example can easily be extended and adapted to
other corpora and other languages.
Tokenization and Sentence Detection (Brown et al. Section 12.2)
First, the corpora are tokenized (steps 4601 and 4602) using a finite-state
tokenizer of the sort described in Subsection 3.2.1. Next, (steps 4602 and
4603), the corpora are partitioned into sentences using a finite state
sentence boundary detector. Such a sentence detector can easily be
constructed by one skilled in the art. Generally, the sentences produced
by such a sentence detector conform to the grade-school notion of
sentence: they begin with a capital letter, contain a verb, and end with
some type of sentence-final punctuation. Occasionally, they fall short of
this ideal and consist merely of fragments and other groupings of words.
In the Hansard example, the English corpus contains 85,061,286 tokens in
3,510,744 sentences, and the French corpus contains 97,857,452 tokens in
3,690,425 sentences. The average English sentence has 24.2 tokens, while
the average French sentence is about 9.5% longer with 26.5 tokens. The
left-hand side of FIG. 42 shows the raw data for a portion of the English
corpus, and the right-hand side shows the same portion after it was
cleaned, tokenized, and divided into sentences. The sentence numbers do
not advance regularly because the sample has been edited in order to
display a variety of phenomena.
Selecting Anchor Points (Brown et al. Section 12.3)
The selection of suitable anchor points (Step 4701) is a corpus-specific
task. Some corpora may not contain any reasonable anchors.
In the Hansard example, suitable anchors are supplied by various reference
markers that appear in the transcripts. These include session numbers,
names of speakers, time stamps, question numbers, and indications of the
original language in which each speech was delivered. This auxiliary
information is retained in the tokenized corpus in the form of comments
sprinkled throughout the text. Each comment has the form .backslash.SCM{}
. . . .backslash.ECM {} as shown on the right-hand side of FIG. 42.
To supplement the comments which appear explicitly in the transcripts, a
number of additional comments were added. Paragraph comments were inserted
as suggested by the space command of the original markup language. An
example of this command appears in the eighth line on the left-hand side
of FIG. 42. The beginning of a parliamentary session was marked by a
Document comment, as illustrated in Sentence 1 on the right-hand side of
FIG. 42. Usually, when a member addresses the parliament, his name is
recorded. This was encoded as an Author comment, an example of which
appears in Sentence 4. If the president speaks, he is referred to in the
English corpus as Mr. Speaker and in the French corpus as M. le President.
If several members speak at once, a shockingly regular occurrence, they
are referred to as Some Hon. Members in the English and as Des Voix in the
French. Times are recorded either as exact times on a 24-hour basis as in
Sentence 81, or as inexact times of which there are two forms: Time=Later,
and Time=Recess. These were encoded in the French as Time=Plus Tard and
Time=Recess. Other types of comments are shown in Table 13.
TABLE 13
______________________________________
Examples of comments
English French
______________________________________
Source = English Source = Traduction
Source = Translation
Source = Francais
Source = Text Source = Texte
Source = List Item Source = List Item
Source = Question Source = Question
Source = Answer Source = Reponse
______________________________________
The resulting comments laced throughout the text are used as anchor points
for the alignment process. The comments Author=Mr. Speaker, Author=M. le
Preesident, Author=Some Hon. Members, and Author=Des Voix are deemed minor
anchors. All other comments are deemed major anchors with the exception of
the Paragraph comment which was not treated as an anchor at all. The minor
anchors are much more common than any particular major anchor, making an
alignment based on minor anchors much less robust against deletions than
one based on the major anchors.
Aligning Major Anchors (Brown et al. Section 12.4)
Major anchors, if they are to be useful, will usually appear in parallel in
the two corpora. Sometimes, however, through inattention on the part of
translators or other misadventure, an anchor in one corpus may be garbled
or omitted in another. In the Hansard example, for instance, this is
problem is not uncommon for anchors based upon names of speakers.
The major anchors of two corpora are aligned (Step 4702) by the following
method. First, each connection of an alignment is assigned a numerical
cost that favors exact matches and penalizes omissions or garbled matches.
In the Hansard example, these costs were chosen to be integers between 0
and 10. Connections between corresponding pairs such as Time=Later and
Time=Plus Tard, were assigned a cost of 0, while connections between
different pairs such as Time=Later and Author=Mr. Bateman were assigned a
cost of 10. A deletion is assigned a cost of 5. A connection between two
names was assigned a cost proportional to the minimal number of
insertions, deletions, and substitutions necessary to transform one name,
letter by letter, into the other.
Given these costs, the standard technique of dynamic programming is used to
find the alignment between the major anchors with the least total cost.
Dynamic programming is described by R. Bellman in the book titled Dynamic
Programming, published by Princeton University Press, Princeton, N.J. in
1957. In theory, the time and space required to find this alignment grow
as the product of the lengths of the two sequences to be aligned. In
practice, however, by using thresholds and the partial traceback technique
described by Brown, Spohrer, Hochschild, and Baker in their paper, Partial
Traceback and Dynamic Programming, published in the Proceedings of the
IEEE International Conference on Acoustics, Speech and Signal Processing,
in Paris, France in 1982, the time required can be made linear in the
length of the sequences, and the space can be made constant. Even so, the
computational demand is severe. In the Hansard example, the two corpora
were out of alignment in places by as many as 90,000 sentences owing to
mislabelled or missing files.
Discarding Sections (Brown et al. Section 12.5)
The alignment of major anchors partitions the corpora into a sequence of
aligned sections. Next, (Step 4703), each section is accepted or rejected
according to the population of minor anchors that it contains.
Specifically, a section is accepted provided that, within the section,
both corpora contain the same number of minor anchors in the same order.
Otherwise, the section is rejected. Altogether, using this criteria, about
10% of each corpus was rejected. The minor anchors serve to divide the
remaining sections into subsections that range in size from one sentence
to several thousand sentences and average about ten sentences.
Aligning Sentences (Brown et al. Section 12.6)
The sentences within a subsection are aligned (Step 4704) using a simple
statistical model for sentence lengths and paragraph markers. Each corpus
is viewed as a sequence of sentence lengths punctuated by occasional
paragraph markers, as illustrated in FIG. 43. In this figure, the circles
around groups of sentence lengths indicate an alignment between the
corresponding sentences. Each grouping is called a bead. The example
consists of an ef-bead followed by an eff-bead followed by an e-bead
followed by a .paragraph..sub.e .paragraph..sub.f -bead. From this
perspective, an alignment is simply a sequence of beads that accounts for
the observed sequences of sentence lengths and paragraph markers. The
model assumes that the lengths of sentences have been generated by a pair
of random processes, the first producing a sequence of beads and the
second choosing the lengths of the sentences in each bead.
The length of a sentence can be expressed in terms of the number of tokens
in the sentence, the number of characters in the sentence, or any other
reasonable measure. In the Hansard example, lengths were measured as
numbers of tokens.
The generation of beads is modelled by the two-state Markov model shown in
FIG. 44. The allowed beads are shown in FIG. 45. A single sentence in one
corpus is assumed to line up with zero, one, or two sentences in the other
corpus. The probabilities of the different cases are assumed to satisfy
Pr(e)=Pr(f), Pr(eff)=Pr(eef), and
Pr(.paragraph..sub.e)=Pr(.paragraph..sub.f).
The generation of sentence lengths given beads is modeled as follows. The
probability of an English sentence of length l.sub.e given an e-bead is
assumed to be the same as the probability of an English sentence of length
l.sub.e in the text as a whole. This probability is denoted by
Pr(l.sub.e). Similarly, the probability of a French sentence of length
l.sub.f given as f-bead is assumed to equal Pr(l.sub.f). For an ef-bead,
the probability of an English sentence of length l.sub.e is assumed to
equal Pr(l.sub.e) and the log of the ratio of length of the French
sentence to the length of the English sentence is assumed to be normally
distributed with mean .mu. and variance .sigma..sup.2. Thus, if r=log
(l.sub.f /l.sub.e), then
Pr(l.sub.f .vertline.l.sub.e)=.alpha.exp -(r-.mu.).sup.2
/(2.sigma..sup.2)!, (181)
with .alpha. chosen so that the sum of Pr(l.sub.f .vertline.l.sub.e) over
positive values of l.sub.f is equal to unity. For an eef-bead, the English
sentence lengths are assumed to be independent with equal marginals
Pr(l.sub.e), and the log of the ratio of the length of the French sentence
to the sum of the lengths of the English sentences is assumed to be
normally distributed with the same mean and variance as for an ef-bead.
Finally, for an eff-bead, the probability of an English length l.sub.e is
assumed to equal Pr(l.sub.e) and the log of the ratio of the sum of the
lengths of the French sentences to the length of the English sentence is
assumed to be normally distributed as before. Then, given the sum of the
lengths of the French sentences, the probability of a particular pair of
lengths, l.sub.f.sbsb.1 and l.sub.f.sbsb.2, is assumed to be proportional
to Pr(l.sub.f.sbsb.1)Pr(l.sub.f.sbsb.2).
Together, the model for sequences of beads and the model for sentence
lengths given beads define a hidden Markov model for the generation of
aligned pairs of sentence lengths. Markov Models are described by L. Baum
in the article "An Inequality and associated maximization technique in
statistical estimation of probabilistic functions of a Markov process",
appearing in Inequalities in 1972.
The distribution Pr(l.sub.e) and Pr(l.sub.f) are determined from the
relative frequencies of various sentence lengths in the data. For
reasonably small lengths, the relative frequency is a reliable estimate of
the corresponding probability. For longer lengths, probabilities are
determined by fitting the observed frequencies of longer sentences to the
tail of a Poisson distribution. The values of the other parameters of the
Markov model can be determined by from a large sample of text using EM
method. This method is described in the above referenced article by E.
Baum.
TABLE 14
______________________________________
Parameter estimates
Parameter Estimate
______________________________________
Pr (e), Pr (f) .007
Pr (ef) .690
Pr (eef), Pr (eff)
.020
Pr (.paragraph..sub.e), Pr (.paragraph..sub.f)
.005
Pr (.paragraph..sub.e .paragraph..sub.f)
.245
.mu. .072
.sigma..sup.2 .043
______________________________________
For the Hansard example, histograms of the sentence length distribution
Pr(l.sub.e) and Pr(flen) for lengths up to 81 are shown in FIGS. 46 and 47
respectively. Except for lengths 2 and 4, which include a large number of
formulaic sentences in both the French and the English, the distributions
are very smooth.
The parameter values for the Hansard example are shown in Table 14. From
these values it follows that 91% of the English sentences and 98% of the
English paragraph markers line up one-to-one with their French
counterparts. If X is a random variable whose log is normally distributed
with mean .mu. and variance .sigma..sup.2, then the mean X is exp
(.mu.+.sigma..sup.2 /2). Thus from the values in the table, it also
follows that the total length of the French text in an ef-, eef-, or
eff-bead is about 9.8% greater on average then the total length of the
corresponding English text. Since most sentences belong to ef-beads, this
is close to the value of 9.5% given above for the amount by which the
length of the average French sentences exceeds that of the average English
sentence.
Ignoring Anchors (Brown et al. Section 12.7)
For the Hansard example, the distribution of English sentence lengths shown
in FIG. 46 can be combined with the conditional distribution of French
sentence lengths given English sentence lengths from Equation (181) to
obtain the joint distribution of French and English sentences lengths in
ef-, eef-, and eff-beads. For this joint distribution, the mutual
information between French and English sentence lengths is 1.85 bits per
sentence. It follows that even in the absence of anchor points, the
correlation in sentence lengths is strong enough to allow alignment with
an error rate that is asymptotically less than 100%.
Numerical estimates for the error rate as a function of the frequency of
anchor points can be obtained by Monte Carlo simulation. The empirical
distributions Pr(l.sub.e) and Pr(l.sub.f) shown in FIGS. 46 and 47, and
the parameter values from Table 14 can be used to generated an artificial
pair of aligned corpora, and then, the most probable alignment for these
corpora can be found. The error rate can be estimated as the fraction of
ef-beads in the most probable alignment that did not correspond to
ef-beads in the true alignment.
By repeating this process many thousands of times, an expected error rate
of about 0.9% was estimated for the actual frequency of anchor points in
the Hansard data. By varying the parameters of the hidden Markov model,
the effect of anchor points and paragraph markers on error rate can be
explored. With paragraph markers but no anchor points, the expected error
rate is 2.0%, with anchor points but no paragraph markers, the expected
error rate is 2.3%, and with neither anchor points nor paragraph markers,
the expected error rate is 3.2%. Thus, while anchor points and paragraph
markers are important, alignment is still feasible without them. This is
promising since it suggests that the method is applicable to corpora for
which frequent anchor points are not available.
TABLE 15
__________________________________________________________________________
Unusual but correct alignments
__________________________________________________________________________
And love and kisses to you, too.
Pareillement.
. . . mugwumps who sit on the fence with
. . . en voulant menagerlachevreetlechoux
their mugs on one side and their
ils n'arrivent pas a prendre parti.
wumps on the other side and do not
know which side to come down on.
At first reading, she may have.
Elle semble en effet avoir un grief tout a
fait valable, du moins au premier
abord.
__________________________________________________________________________
Results for the Hansard Example (Brown et al. Section 12.8)
For the Hansard example, the alignment method described above ran for 10
days on an IBM Model 3090 mainframe under an operating system that
permitted access to 16 megabytes of virtual memory. The most probable
alignment contained 2,869,041 ef-beads. In a random sample 1000 the
aligned sentence pairs, 6 errors were found. This is consistent with the
expected error rate of 0.9% mentioned above. In some cases, the method
correctly aligned sentences with very different lengths. Examples are
shown in Table 15.
ALIGNING BILINGUAL CORPORA (Brown et al. Section 13)
With the growing availability of machine-readable bilingual texts has come
a burgeoning interest in methods for extracting linguistically valuable
information from such texts. One way of obtaining such information is to
construct sentence and word correspondences between the texts in the two
languages of such corpora.
A method for doing this is depicted schematically in FIG. 48. This method
comprises the steps of
4501. Beginning with a large bilingual corpus;
4504. Extracting pairs of sentences 4502 from this corpus such that each
pair consists of a source and target sentence which are translations of
each other;
4505. Within each sentence pair, aligning the words of the target sentence
with the words in a source sentence, to obtain a bilingual corpus labelled
with word-by-word correspondences 4503.
In one embodiment of Step 4504, pairs of aligned sentences are extracted
using the method explained in detain in Section 12. The method proceeds
without inspecting the identities of the words within sentences, but
rather uses only the number of words or number of characters that each
sentence contains.
In one embodiment of Step 4505, word-by-word correspondence within a
sentence pair are determined by finding the Viterbi alignment or
approximate Viterbi alignment for the pair of sentences using a
translation model of the sort discussed in Sections 8-10 above. These
models constitute a mathematical embodiment of the powerfully compelling
intuitive feeling that a word in one language can be translated into a
word or phrase in another language.
Word-by-word alignments obtained in this way offer a valuable resource for
work in bilingual lexicography and machine translation. For example, a
method of cross-lingual sense labeling, described in Section 11, and also
in the aforementioned paper, "Word Sense Disambiguation using Statistical
Methods", uses alignments obtained in this way as data for construction of
a statistical sense-labelling module.
For each pair of corresponding source and target sentences in the training
text, the estimated most probable alignment between the source words and
the target words is found using the method described above used by the
alignment identifier 18. Each occurrence of a target word e.sub.a.sbsb.j
in the training text is then identified and tagged with the source word
f.sub.j connected to the target word e.sub.a.sbsb.j in each alignment. The
target word e.sub.a.sbsb.j is also tagged with its context X in each
target sentence.
Table 10 illustrates a hypothetical example of training events for a target
word e.sub.a.sbsb.j ="key" in a training text of aligned source language
sentences and target language sentences.
TABLE 10
__________________________________________________________________________
TRAINING EVENTS
Occurrences of e.sub.aj = "key" in a training
text of aligned source and target sentences, the source
word f.sub.j connected to "key" in each alignment,
and the context X of "key" in each target sentence.
X = {e.sub.(aj-3), e.sub.(aj-2), e.sub.(aj-1), e.sub.(aj+1),
e.sub.(aj+2), e.sub.(aj+3) }
f.sub.j
e.sub.aj
x.sub.1
x.sub.2
x.sub.3
x.sub.4
x.sub.5
x.sub.6
__________________________________________________________________________
clef
key <null>
My car is on the
clef
key make me a for my door
ton key she sings
in <null>
<null>
<null>
clef
key <null>
<null>
The unlocks
the safe
ton key play in the of B flat
ton key finger
on the of middle
C
. . . . . . . .
. . . . . . . .
. . . . . . . .
__________________________________________________________________________
Using the training events for a target word e.sub.a.sbsb.j from the
training text, candidate context functions g.sub.i (f,e,X) may be obtained
by first identifying types of context functions. For example, one type of
context function tests for the presence of a particular word in one or
more positions in the context. Another type of context function tests for
the presence of a particular word class (for example, part of speech) in
one or more positions in the context. The particular words or word classes
to be tested for in a context function for a target word e can be obtained
from the words and classes in the context of the target word e in the
training text.
Alternatively, candidate context functions g.sub.i (f,e,X) may be obtained
by clustering the training events according to their context X using the
method described in Section 7 of Brown et al (cited above), which is
incorporated herein by reference.
Initially, all of the parameters .lambda..sub.(e,i) of the word match score
model of Equation 7 are set equal to zero.
For each candidate context function g.sub.i (f,e,X), a "measure of merit"
G(i) is calculated to according to Equation 11.
##EQU56##
In Equations 11 through 14, the conditional probability P(f.vertline.X) of
a source word f given the context X of the connected target word e is
obtained from Equation 7 using the most recent values of the model
parameters. The probability P(X) of a context X is obtained by counting
occurrences of the target word e and the context X in the training text
events of the type shown in Table 10, and dividing the count by the total
number of events in the training text in which the target word is e. The
probability P(f,X) of a source word f and a context X are obtained by
counting occurrences of the target word e, the context X, and the source
word f in the training text events of the type shown in Table 10, and
dividing each count by the total number of events in the training text in
which the target word is e.
The context function g.sub.i (f,e,X) having the highest "measure of merit"
G(i) from Equation 11 is selected as a context function to be used in
Equation 7. The parameter .lambda..sub.(e,i) is obtained by initially
setting .lambda..sub.(e,i) =0, and then solving the following Equation 15
for the quantity .DELTA..lambda..sub.(e,i).
##EQU57##
A new value of .lambda..sub.(e,i) is obtained by adding the value of
.DELTA..lambda..sub.(e,i) to the previous value of .lambda..sub.(e,i).
Using the new value of .lambda..sub.(e,i), Equation 15 is then again
solved for a new value of .DELTA..lambda..sub.(e,i). The process is
repeated, each time updating the value of .lambda..sub.(e,i), until the
value of .DELTA..lambda..sub.(e,i) falls below a selected threshold. This
method is known as iterative scaling.
Using the new model for the word match score (Equation 7), the "measures of
merit" G(i) of Equation 11 are recomputed for the remaining candidate
context functions g.sub.i (f,e,X) to identify the remaining context
function having the highest "measure of merit". The best remaining context
function is added to the word match score model of Equation 7 and new
values of all of the parameters .lambda..sub.(e,i) are calculated using
the iterative scaling method and Equation 15. When the word match score
model of Equation 7 contains two or more parameters .lambda..sub.(e,i),
every parameters .lambda..sub.(e,i) is updated exactly once per iteration,
so all parameters .lambda..sub.(e,i) converge in the same iteration. The
process is repeated on the remaining candidate context functions g.sub.i
(f,e,X) until the "measure of merit" of the best context function falls
below a selected threshold.
In the translation apparatus according to the present invention, the target
hypothesis generator 12, the language model match score generator 16, the
alignment identifier 18, the word match score generator 20, the
translation match score generator 22, and the hypothesis match score
generator 24 may be suitably programmed general purpose or special purpose
digital signal processors. The target language vocabulary store 14 may be
computer storage, such as random access memory. The means for transforming
the input series of source words into a series of transformed source words
in the source text input device 10, and the means for synthesizing a
series of output words from the target hypothesis having the best target
hypothesis match score of the output 26 may also be suitably programmed
general purpose or special purpose digital signal processors.
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